Where does enterprise technology go at the end of its lifecycle? The answer, as it turns out for some, is sleepy Erskine. Just a 20-minute drive outside of Glasgow, Scotland, HPE’s Technology Renewal Centre (TRC) is where end-of-life technology and components get cleaned, wiped, and reused in other hardware, or recycled.

It is the main operational hub for HPE Financial Services (HPFS), the company’s financing and lifecycle management arm, which provides closed-loop solutions for its enterprise customers.

The 240,000 sq ft facility is all that remains of Silicon Glen, a local technology innovation hub that operated from the 1940s to the 1990s when American defense and electronics firms first expanded into Europe post-war.

First acquired by HPE in 2011, the facility processes 150 pallets and around 8,000–9,000 individual assets daily, running on a 90% wholesale and 10% certified pre-owned remarketing model. Alongside its similarly-sized sister site in Andover, Massachusetts, the TRCs are global centers of excellence, supported by around 40 local regional intake sites worldwide. Assets are returned to the facility as part of HPE’s IT Asset Disposition Services or at a customer’s end of lease, the latter of which makes up 56% of the assets returned.

This year to date, the TRCs have processed 3.7 million assets, two-thirds of those being servers and PCs, according to HPE. The company says 4.3 million assets were processed globally in 2025, with a mammoth 84% going on to be reused in some way, or refurbished and resold.

Built on a site with a rich manufacturing history – a former Compaq and HP manufacturing facility – the facility’s roots can still be seen within its operations, with almost 70% of workplace assets that come through its doors being HP branded.

The site started with a mere 40,000 sq ft and a fleet of 50 highly skilled employees, eventually growing to a 200-strong workforce, says Jackie Rafferty, TRC operations manager at HPE Financial Services, who himself has been with the facility for 39 years, as he delivers one of many facility tours HPE offers prospective customers.

“The reinvention of this site has been really impressive for decades,” says Matt Harris, SVP and managing director for UKIMEA at HPE, who also started his career at this very site 23 years ago. “It aligns quite well to the conversations we're having now as a major technology renewal center [which] are all around reinvention and a new lease of life”.

The AI boom and a shifting economy

HPE has found a new burgeoning market over the last five years, thanks in part to the increasing global demand for components – largely driven by the rapid investment in AI infrastructure and development. At the same time, tightening macroeconomic conditions are changing how businesses procure this highly sought-after hardware.

“Customers who previously bought cash are looking to buy and use financing,” says Maeve Culloty, president and CEO at HPFS, in her opening presentation before the tour. “HPFS has been financing GPUs for almost 10 years, but the renew market wasn't in existence [back then] and the value of residual [tech] wasn't very clear.”

“About five years ago, we started making major investments and helping our customers acquire GPU technology. What was unclear and uncertain at the time was can we carry a residual value or risk on that technology, and can we see a secondary market for those GPUs? If I were here three years ago, I would have struggled to answer that question…”

“The concern was that the market would have been flooded by the initial investment into H100s and H200s, and that they would come to the secondary market. Now it's super clear: there is a secondary market for GPUs… Our customers do want access and are very happy to take second-life GPUs.”

To meet this demand – and to answer EU and UK sovereign requirements – Culloty says HPE is currently building out its initial GPU capacity and testing infrastructure in Boston. In time, it hopes to bring those capabilities to Europe.

“As workloads move between GPUs and CPUs, we expect continued demand with regard to the performance of AI infrastructure and GPUs,” she notes.

Building trust in ‘Certified pre-owned’

Historically, the industry has had a complicated relationship with refurbished technology. While in theory it makes a great case in a circular economy and for cost-saving, operational stigmas persist, with many procurement teams hesitant to risk pre-owned tech for long-term investment, particularly when it comes to data hygiene.

Leading the tour through the facility’s 2.2m perimeter fence, Rafferty highlights that building trust is Erskine's true core product. “What we see today is data security gaining a bit of a competitive advantage,” he says.

Because of the confidential nature of the memory and storage components it works with, the facility takes every precaution to ensure safety and security. The TRC leverages its roots once again, testing all refurbished units using the exact same OEM-grade testing infrastructure used on Compaq’s original manufacturing lines and is ISO 27001 compliant.

This guarantees that pre-owned equipment meets factory performance standards. HPE notes that the facilities are fully licensed, consistent with HMG IA Standard No. 5 sanitization, and subject to regular audits.

“Everything you see here is appropriately subdivided with extensive cybersecurity because of the money involved in that. Everyone in the operation has been security cleared by the Ministry of Defense. So, we've got high-caliber, vetted staff with huge amounts of experience,” says Rafferty, whose passion for the facility and its workforce is palpable. “This is probably the most flexible staff I've ever had in the last 15 years.”

“In the operations, we have 40 360-degree fisheye cameras in the area. There are infrared alarms and fire alarms across all the fence lines. There are vibration alarms – there's everything,” he explains.

“It's really a top-tier physical security install, really just to showcase to our customers that we are serious about data and willing to use professional equipment to maintain the standard.”

But beyond physical fences, the Erskine facility relies on a rigorous chain of custody to secure the customer's peace of mind. As David Connell, engineering manager at HPE, notes during the tour, the two most top-of-mind questions for customers are “data security, and the second one is data security”.

He explains that the moment a customer's equipment arrives at the dock door, it is registered using barcode scanners, instantly generating a receipt acknowledgment report so the customer has immediate visibility. These assets' serial numbers continue to trace chain of custody, repairs and modification, data sanitation records and the final disposition.

To date, HPE says it has securely sanitized 1.7 exabytes of data – that’s 1.6 million drives overwritten, equivalent to watching around 97 million years of HD movies.

But not every customer is convinced on paper. That’s why Rafferty believes in the power of bringing customers into the operation to see things for themselves. Indeed, the center hosts around 50 customer tours per year.

“Seeing is believing,” he says, adding that every tour they’ve completed has led to a new customer signing a contract with them. “That's where trust comes in… we want to deliver that to customers.”

Real-world value and the circular economy

When customers do sign on, they quickly find that this refurbished technology and flexible financing play a vital role in meeting corporate sustainability targets.

During his presentation, Karim Abu-Dahab, principal technologist, sustainable transformation at HPE, pointed out that “green IT” is often misunderstood by the industry. “Any gain in efficiency that our customers may have almost automatically translates into a gain in sustainability,” he explains.

“A while ago, when it came to IT, after I'm done with a server we just throw it away, right? Now this is not the case anymore,” he says. In fact, HPE estimates that over 8.4 million kg of waste has been kept from landfill – equivalent to the annual e-waste of over 1 million people.

Not only are companies able to support their environmental goals by avoiding landfills, they are also able to recover value from their returned assets, “and then the cycle goes on and on,” Abu-Dahab adds.

“Every year, HPFS hands back about $1 million a day to our customers in order to fund the next innovation,” says Culloty. “We look at both their leased and non-leased environments, and if there's value in that current infrastructure, we return it back to the customer.”

HPE says that through this value return system, $1.25 billion has been infused back into customer budgets between 2020 and 2025. Harris offers the example of Liverpool John Moores University as one customer making the most of this cash injection. He explains the university recently retired old assets and used the resulting money to increase their student hardship loan facilities.

This isn’t the only way HPE is proving refurbished technology can have serious business value. According to Culloty, HPFS’s 90/9 financing model “allows customers to acquire the technology today and pay a very low rate of financing while they deploy that product over a period of time”. With this system, a customer pays nothing in the first 90 days, and then pays one percent for the following nine months.

Rob Pickering, CTO at Tottenham Hotspur FC, is one of HPE’s customers who leveraged this flexibility to support the club's goal of reaching net-zero by 2040. Pickering says the club is currently two-thirds of the way through replacing 63 server rooms and eight data centers.

“From a footprint perspective, we're probably about 60% smaller... because the technologies have become more capable, so you get more cycles per rack effectively,” he says.

Rather than sending the old hardware to a landfill, the club is injecting it back into the secondary market to realize its remaining value and fund ongoing innovation. “Simply running equipment that's eight or nine years old is significantly more expensive than running the new equipment,” he adds.

Indeed, HPE estimates over the course of its operations that it has saved over 29M kWh of energy – enough to charge an EV 486,000 times.

“I think there's a world where we pay almost entirely for the new assets from the power savings… and then we can pay back some of that equipment that we're recycling.”

This is not only bringing down costs, but also directly contributing to the club’s sustainability goals. “It's a win-win-win,” he says.

“We want to be innovative, we want to be sustainable, we want to make sure we deliver the best experiences to our fans and HPE helps us do all of those in a way that's financially responsible and environmentally responsible.”

Halloween is upon us, and October has already been full of IT horror stories. French supermarket fridges were filled with spoiled cheeses after internet-connected temperature controls were hacked. And another hyperscaler outage taking down banking services.

While they are different and unique incidents, both raise questions of reliability and safety. Could they have been avoided? Will they happen again? And what can we learn from them?

On this week's episode, ITPro news and analysis editor Ross Kelly joins Bobby and Scott to talk through the biggest stories of the month.

Highlights

"I'm very much of the opinion that some things don't need to be connected to the web. You don't need to have a sprawling IoT web in your home. That's just opening the doors to attackers."

"A lot of the time, when we talk about big hyperscaler outages, the first thing that comes to your head is US East 1. Because when something goes down across the Atlantic, a lot of the time in Europe there's an impact there."

Links

• French fridges hacked in country-wide supermarket attack
• Google Cloud outage explained

Every AI agent entering a customer environment becomes part of its digital workforce. It can hold credentials and make decisions, yet its identity may never appear in the processes used to govern employees.

That gap matters because agents are moving from controlled pilots into everyday operations. They are connecting to collaboration platforms and business-critical systems where they can send messages and alter records at machine speed. The permissions behind those actions often receive less scrutiny than those given to a new employee.

For Managed Service Providers (MSPs) and solution providers, this changes the customer conversation. Partners need to know what each agent can reach and who remains accountable for its actions.

Many customers will struggle to answer those questions because agents may have appeared through SaaS features or experiments run outside IT. This blurs ownership as a pilot grows, while its original access follows it into production. By the time an auditor starts asking questions, the business may depend on that workflow.

Security researchers recently demonstrated how GitHub Agentic Workflows could be manipulated into retrieving information from private repositories and publishing it in public comments. A routine issue became a route between untrusted input and protected information because the agent could access both.

The case should focus partners’ attention on the architecture around an agent. Every connected system can widen its reach and increase the potential impact if it is compromised or behaves unexpectedly.

Temporary access has a habit of becoming permanent

The route to over-privilege often begins with a reasonable decision. For example, a team wants to prove that an agent can deliver value, so it grants access through an existing service account or API key.

Problems emerge when that access outlives the assumptions behind it. The pilot enters production, yet its original token stays active. New tasks bring broader permissions, while ownership becomes harder to trace.

Traditional joiner, mover, and leaver processes offer little help. Employees have managers and defined roles, but agents may be created inside a SaaS platform, inherit their creator’s permissions, and remain active after that person changes position. Agents need lifecycle events of their own.

OWASP’s guidance for agentic applications identifies identity and privilege abuse as a major threat, covering the misuse of delegated permissions and inherited access. Autonomy gives these long-standing identity weaknesses a larger blast radius.

Our research, surveying 800 IT leaders across the UK and US, found that non-human identities outnumber employees in 83% of organizations. Only 21% have controls in place to govern them. Human oversight is also becoming less common, with a quarter of organizations requiring human review before high-risk AI actions, compared with 40% six months earlier.

These findings should prompt partners to reconsider where an AI project ends. Installing an agent marks the beginning of an access-management responsibility that may last for years.

Give every agent an owner and an expiry date

Partners can start by helping customers build an inventory of the agents operating across their environments. This should include the credentials each one uses and the systems within its reach. Every production agent needs a named business owner, with its purpose recorded in language the customer can understand.

A distinct identity should replace borrowed or shared credentials where the platform supports one. Permissions can then reflect the agent’s job instead of copying the access held by the employee who deployed it.

Least privilege also needs to account for actions. An invoice-processing agent may need to read documents and update selected fields. Changing supplier bank details belongs behind an approval gate. The same principle applies wherever an agent can make an irreversible decision or expose sensitive information.

Partners should plan for the moment when an agent moves outside its intended role. Customers need a tested way to suspend its identity and revoke access. Searching across disconnected systems during an incident is an expensive way to discover that no kill switch exists.

Access should also have a shelf life. Permissions that made sense during deployment may become redundant as workflows change. Reviews tied to the agent’s risk level can remove dormant access before it fades into the background.

In practice, this means helping customers discover their agents, register them with named owners, manage their access and govern their actions over time.

The channel can make agent governance operational

Customers are adopting agents faster than many internal teams can catalogue them. Partners already understand their systems and identity environments, putting them in a strong position to close that visibility gap.

MSPs can add non-human identity discovery to security assessments and then build access reviews into managed services. Solution providers can design role-based policies before an agent reaches production. Managed Security Service Providers (MSSPs) can bring agent activity into monitoring and response processes.

Partners should apply the same discipline to their own operations. An MSP’s internal agent may connect to tools with privileged access across customer accounts. Its permissions deserve as much scrutiny as those of an engineer with administrative rights.

Ultimately, AI adoption will continue because customers can see the productivity gains. Sustaining those gains depends on whether they can trust the access behind them.

Partners that make ownership visible and keep permissions aligned with purpose will offer something more valuable than an AI deployment, and that’s the confidence to put automation into production without surrendering control.

To many enterprises, AI costs – from compute to tokens, licenses, integration, and data – are now a familiar part of company expenditures, as we approach four years since OpenAI released ChatGPT, kickstarting the explosion of generative AI.

Just below the surface, however, is a less visible layer of costs: approvals, testing, audit trails, human review, and documentation all take time and resources, while weak or late governance can lead to rework, additional tooling, and delays to production.

The rise of capable AI models and, in recent times, AI agents that can carry out increasingly complex tasks with less human intervention means that, as pilots move into production, AI governance is now an extra workload spanning IT, security, legal, risk, and data teams.

AI governance is not the highest cost for companies – tokens and licenses are likely to rank higher – but it is an increasingly important part of AI’s total cost of ownership (TCO), and is naturally spread across a variety of teams and processes.

To make sense of the hidden costs associated with the latest AI wave, ITPro spoke to Enza Iannopollo, VP and principal analyst at Forrester, and David Menninger, executive director of software research and distinguished analyst at ISG.

The two analysts share a similar argument: governance is necessary, but the actual costs can vary significantly depending on how and when it is introduced. Controls that are added too late can mean months of additional work, while manual governance can itself become a bottleneck.

How governance gets expensive

As is so often the case in both life and in business, foresight is key, and many of the biggest AI governance costs – monetary, staff time, and otherwise – stem from adding controls too late in the process.

“Unfortunately, in many enterprises, the biggest cost has been retrofitting governance into AI deployments,” says ISG’s Menninger.

“When AI governance is not built into the project from the start, it can add months to the project rollout.”

“Our research of 1,200 AI use cases shows that about one third have made it into production,” Menninger continues. “The good news is that’s double what it was a year ago. The bad news is that two-thirds are still not in production.”

That gap between experimentation and production is where hidden costs can start to pile up.

“The same study indicates that, on average, it will cost twice as much as has been spent already and will take more than a year to complete,” he says.

“These figures suggest that much [of the] cost is not anticipated and that enterprises are learning as they go.”

Governance is only one part of that extra spend, but retrofitting it after the fact adds another speed bump just as companies are trying to move beyond the pilot stage.

Forrester’s Iannopollo argues that the answer is not so much to govern less, but to govern earlier: “Failing to scale an AI pilot is one of the most common costs organizations pay for not building the right governance from the start.”

“Relying on solid AI governance from the start means ensuring the appropriate controls and guardrails are in place,” she says.

“Pilots are the right moment to test them, adjust them, and prepare them to enable scale.”

Having a “human-in-the-loop” comes at a price

AI-driven automation is perhaps the hot trend in 2026, with companies increasingly experimenting with ‘swarms’ of powerful AI agents able to carry out work that until recently would have required human input – but people are still heavily involved in checking the eventual outputs.

In other words, automation does not necessarily remove labour; in many cases, it simply shifts where that labour sits.

“ISG Market Lens research indicates that 90% of AI-enabled work still involves humans, either with AI providing assistance or with humans reviewing and approving the outputs,” says Menninger. The bill for supporting all this human oversight naturally lands on people and the manual processes they perform.

The irony is that AI is often best at automating the most mundane tasks, leaving the hardest work for humans. “The manual review process is typically only needed for the most complex scenarios, so it’s no surprise that those would take more time and effort,” he says.

In effect, the easier work can disappear while the expensive judgement calls remain.

“Human review thresholds seem to be one of the most successful techniques for managing cost and risk,” says Menninger.

“It enables enterprises to focus their resources on the areas with the most exposure.”

The aim, then, is to concentrate their attention where the potential consequences are greatest, not to remove humans from oversight entirely.

“As AI-enabled processes become more accurate and more refined, the percentage of AI-related decisions requiring review will decrease, but our research suggests it will take time. By year-end 2027, only 13% of AI-enabled work will be fully autonomous.”

Iannopollo likewise says human oversight must be targeted: “Humans must focus on reviewing and approving high-risk use-cases, responding timely in situations where AI performance and/or behavior drift, and acting with full authority to ensure the business keeps running, even if they have to ‘kill’ an LLM, an agent, or an entire agentic workflow.”

Manual governance can be a bottleneck

Operating at a small scale, ideas like central boards or manual checks can work well; the problems emerge when companies try to scale them across the organisation. A model that works for, say, ten AI projects can quickly break at 100.

“AI governance that relies only on manual checks and processes becomes a bottleneck,” says Iannopollo.

“Governing AI requires [the] implementation of technical controls, automation, and careful distribution of human-led activities.”

It’s at this point that AI governance moves beyond policy documents and (human) committees and becomes something operational and technical – embedded in the systems used to manage approvals, enforce data and access controls, maintain auditability, and monitor AI in production.

“Our research suggests that enterprises need to supplement the governance provided by AI software platforms and tools,” says Menninger

“For example, only one-third of more than two dozen vendors evaluated fully met our criteria for agent evaluation tools. Less than one-fifth met our criteria for A/B testing and less than half met our criteria for human-in-the-loop reviews.”

“As a result, we recommend that enterprises anticipate they will need to incorporate additional tools and processes to complete their governance over AI,” he says.

Dedicated governance tooling is already becoming a sizable market in its own right: Gartner forecasts spending on AI governance platforms will rise from $275 million in 2026 to $462 million in 2027.

“The other common misunderstanding is that governance always means a lot of processes, controls, and documentation,” says Iannopollo.

“This is certainly true for some use-cases, but not for most. Assessing risks of AI use-cases early means that the measures can be implemented in a proportionate manner.”

“Starting from low-risk use cases increases the likelihood of success, further strengthening the confidence of the organization in AI,” she says.

The scalable option, then, is a risk-tiered governance model that uses automation to reduce the routine burden while concentrating human oversight and heavier controls on higher-risk use cases.

Governance should be an enabler

The costs of AI governance – labour, delayed rollouts, more tooling, oversight – are now clear; the answer for companies is not to minimise governance at all costs.

“Too often [organizations] embrace the rhetoric about governance as a road blocker and an inhibitor of innovation,” says Iannopollo.

“The truth is that in most cases lack of governance is the road blocker.”

Just as companies price in the costs of compute, data, integration, licenses, and so on, governance should be priced into AI projects from the outset.

Making governance proportionate and repeatable is the key, and the later companies leave it in the process, the greater the risk of costly rework and delays. A repeatable, scalable governance model is the dream – one that can grow with AI adoption without requiring the same increase in manual oversight.

Anthropic is set to go public in late November at a valuation of between $1.5 trillion and $2 trillion. The higher end of the range would make it the biggest initial public offering (IPO) ever, beating SpaceX’s $1.8 trillion public debut in June. The Claude creator filed its much-awaited IPO prospectus on Monday 26 October, giving investors and tech leaders a peek into its financials, especially its dependencies.

The document, also known as an S-1, highlights a reliance on a limited number of customers for its revenue. Nearly half (47%) of sales in 2025 were routed through its cloud partners, Amazon and Alphabet’s Google. Meanwhile, each of its two biggest customers, who remained unnamed, accounted for 12% of revenue last year.

Some progress is being made to reduce this dependence. While 48% of revenue between January and September this year came through its cloud providers, the five biggest customers accounted for approximately 25% of revenue for the nine-month period. No single customer had a share greater than 10%.

The company noted in its filing: “We anticipate that we will continue to derive a significant portion of our revenue from a limited number of customers for the foreseeable future, and in some cases, the portion of our revenue attributable to certain customers may increase in the future.”

The IPO prospectus has raised questions about an overreliance on AI models and vendors – and the risks this brings for enterprises.

“It’s a reminder that the model, the vendor and the route you access it through can all change for reasons outside your control,” cautions Jan Karstens, CTO at AI-powered SAP automation platform Avantra. For example, enterprises that have built their critical business processes around one AI supplier could face operational disruption, even if it’s temporary.

“We’ve already seen what that looks like. When Anthropic was ordered to block foreign nationals from its top models this summer, the only way to comply in time was to switch them off for everyone. Teams that had already assumed any model or vendor could disappear overnight barely noticed. Everyone else had to scramble,” says Karstens.

Charles Radclyffe, CEO of SaaS enterprise provider EA Global AI adds: “The interesting thing about the reported Anthropic numbers is that they're not just a story about one AI company. They're a reminder that concentration risk runs through the AI ecosystem, and enterprises need to understand where those dependencies sit.”

Earlier this year, automation platform Zapier polled 500 US executives and found that 47% of enterprises would stop functioning properly if their primary AI vendor were to shut off its service tomorrow. Just over a quarter (27%) indicated they were reliant on AI vendors for nearly all of their business operations.

Question vendors’ own dependencies

Part of the problem is that enterprises focus too much on model performance, when they should be questioning their AI service’s supply chain.

“If a provider depends heavily on a small number of cloud platforms, strategic partners or major customers, changes in those relationships can ultimately affect enterprise users through pricing, availability, product direction, or service levels,” says Radclyffe.

So, what does he think CIOs should be doing? Asking companies supplying the AI services about their own critical dependencies, how workloads are delivered, the knock-on effect if key commercial relationships change, and what notice customers receive before major model changes. “AI procurement needs to become a resilience conversation,” he stresses.

Karstens believes asking model makers about their critical dependencies is reasonable, but he cautions against acting on their responses alone. He’d “be mildly nervous about concentration among my direct AI vendors. I’d answer that with my own visibility and multiple suppliers, not with questionnaires.”

CIOs need to “start from the assumption that any model, vendor or access path can be withdrawn without warning,” he adds. “They should build governance that doesn’t depend on any one of them staying in place. Periodic audits and vendor assurances after the fact aren’t enough when the [AI] market moves fast.”

Be wary about vendor lock-in

A key takeaway from Anthropic’s S-1 is that consumption-based, or per-token, revenue made up 83% of revenue in the first six months of 2026. However, many of its main customers are not locked into long-term contracts.

AI model makers are pushing hard for customers to agree to contracts to secure multi-year revenue visibility amid fierce competition. If enterprises find themselves signing contracts, then they need to ensure they agree to “meaningful exit rights, portability provisions, and the ability to export not just data but the prompts, workflows and evaluations that support business processes,” advises Radclyffe.

Karstens agrees. “Vendors like Anthropic are moving deeper into everyday work, with documents, slides and projects now living inside the AI tool itself. If that continues, data portability will become a real issue for CIOs in the next 12 to 24 months.”

The Zapier study also surveyed senior executives about AI vendor lock-in. Just over eight in 10 (81%) admitted they were at least slightly nervous about being locked into contracts, with 29% very concerned. Almost half (46%) were worried about data migration and an overreliance on a single vendor. Sudden price hikes were a concern for 41%, while another major worry for 37% was having a single point of failure.

Ideally, enterprises should avoid finding themselves in a situation where these worries come to fruition by ensuring they have a practical alternative to turn to if costs rise, there’s a change in circumstances, or unexpected events.

Radclyffe sums it up perfectly: “There's nothing unusual about [vendor] concentration in a fast-growing technology market. The risk comes when enterprises don't understand how dependent they've become until they're forced to make a change.”

Containers are no longer the new story. For most customers, containerization is already embedded in the way software is built and delivered.

More than half of organizations now use containers for most or all of their production applications, according to the Cloud Native Computing Foundation. That means the challenge for partners is no longer persuading customers to adopt containers, but helping them ensure the way software is being developed today does not create security and governance problems later. Development teams are under pressure to move faster than ever. They are using more open-source components, pulling in code from multiple sources, and increasingly using AI to help write, test, and review software.

Docker's Latest State of Application Development Report found that 64% of developers were already using AI to support coding, documentation, and research.

None of that is a problem in itself. The risk arises when security and governance don't keep pace. Small compromises that seem reasonable at the time can gradually introduce bigger problems. A team takes a shortcut to hit a deadline. Someone pulls in a component that hasn't been properly checked. Neither looks particularly serious in isolation, but together they can leave customers exposed.

A bigger conversation

This is why the conversation is no longer just about containers. It is about how modern software is being built, where code and components are coming from, and what controls are in place around AI use.

AI is a good example of this. It's helping development teams move faster, but it's also changing the questions organizations need to ask. Where did that code come from? Has anyone reviewed it properly? And were your security policies written for the way development teams are working today, or for an approach that's already changed?

The issue does not stop with developers. Across the wider business, employees are starting to use AI tools to speed up everyday work, creating a broader governance challenge.

The question is not simply whether AI can improve productivity, but whether organizations have the guardrails and visibility to ensure it is being used safely. The rapid evolution of AI agents has only added to those concerns. If AI agents can act in ways their users did not expect, organisations need to think more carefully about runtime visibility, access boundaries and what happens if those systems step outside the rules they were meant to follow.

That is exactly where partners should be adding value.

Most customers will not ask how to reduce security debt or tighten AI governance. They will ask how to modernize applications, speed up development, or get more value from AI.

The opportunity for the channel is to help them achieve those goals without creating unnecessary risk at the same time. That starts with asking the right questions. These are not just technical questions. They are business questions because the impact of getting them wrong is operational, financial, and reputational. Partners don’t need to be software developers, but they do need to understand how software is being built and how AI is being used today.

Customers are looking for help to move quickly without losing control. In that environment, experience, judgement and the ability to ask the right questions are just as valuable as the technology itself.

The wider issue is trust. Modern applications depend on increasingly complex software supply chains, and AI adds another layer of uncertainty if businesses do not know what tools are being used, what data they can access, or how decisions are being made. Security cannot begin once something is already live. By then, many opportunities to prevent problems have already passed.

We know that customers will always want to move faster. That’s not going to change. The partners that stand out will not just be the ones recommending the latest tool. They’ll spot risks early, ask the questions their customers haven’t thought about yet, and help to build security and governance into the process from the start.

Sometimes that simply means asking a few awkward questions before anyone starts building. Those conversations may not always be comfortable, but they are far easier and less expensive than trying to fix problems once software is already live.

Five questions every partner should ask

• Where are your container images coming from, and who is verifying them?
• How much third-party or open-source code is making its way into production?
• Has AI changed the way developers write, review, or ship code?
• Are employees using AI tools outside development without clear guardrails in place?
• If an AI agent behaved unexpectedly, would you have the visibility to detect it quickly?

Ultimately, the partners that add the most value will be those who help customers move faster without losing control. As software development evolves and AI becomes more deeply embedded, that means identifying risks early and making security and governance part of the process from the outset.

Earlier this month, Japan’s government declared a cyber emergency. The move followed a series of cyber attacks against businesses and institutions across the country. Rail transport, internet services, retailers and even the police have been impacted, according to the Financial Times.

At one point, the Japanese financial services regulator even told banks not to accept driving licences as proof of identity for anyone opening new accounts.

The declaration by Japan’s NCHS cyber agency is unusual. Few countries have declared a cyber emergency, even if security services and regulators have warned of heightened threats.

The move came after rail operator JR East reported the loss of 1.67 million email addresses, and car rental company Times Car lost a large number of scans of its customers’ driving licences. In addition, cloud services operator IDC Frontier reported a ransomware attack. This, in turn, linked to the leak at JR East and other public sector bodies.

“Incidents like this highlight why supply chain attacks can be so disruptive,” Alexandra Rose, head of global affairs and policy at cybersecurity company Sophos told ITPro.

“An attacker doesn't need to compromise a bunch of organizations individually in order to have a widespread impact. They gain access to a single organization and then can move through interconnected systems or disrupt services that many other organizations depend on. We’ve seen the downstream consequences extending beyond the initial victim time and time again, affecting operations, customers, and more.”

Big in Japan?

Japan, though, is not generally viewed as more exposed to cyber attack than other advanced economies, experts say. But the country is certainly seeing an increase in the seriousness of attacks, if not their frequency. There is also evidence that some businesses, at least, were less well prepared.

“I think it’s a response to the increasing severity of attacks they are seeing,” Rob Demain, founder of security firm e2e-assure, told ITPro. “One of the key ones was the platform IDC Frontier that was taken offline. I think it’s a trend of more impactful cyber attacks being seen, and trying to move quicker and keep pace.”

The Japanese government also moved to bring in active cyber defense (ACD), where police and other organizations can go after attackers, taking down servers and, hopefully, neutralizing attacks. Japan is also considering wider changes to its cybersecurity strategy, after lawmakers recently called to strengthen its use of AI for cyber defense.

But publicly announcing a cyber emergency brings its own risks, not least that businesses may rely on government intervention instead of taking proactive measures themselves. And useful advice could be lost in increased noise.

“How many times can you cry ‘cyber emergency’ before people stop worrying about it?” asks Demain. “But I don’t think there has been anything similar in Japan. So I think if you hear that message, you are going to take notice… You need to make sure everyone’s taking this seriously, and that was just one method to get the message out.”

Not everyone is convinced, however.

“A cyber emergency declaration has value if it changes the response, not just the rhetoric,” cautions Nazir Esmail, chief autonomous enterprise officer at Sapiens, a business that develops software primarily for the insurance industry.

“CISOs are well versed in threat vectors and don’t need additional warnings that threats are on the rise. What would be useful to CISOs and the security community in general, is faster access to actionable intelligence. In addition, if attacks threaten critical services or infrastructure, a coordinated incident response effort and a clear escalation mechanism are essential to contain the impact and accelerate recovery,” he told ITPro.

Actions, not words

This poses the question of whether declaring an emergency has a practical value for enterprises, and the wider security community. If it brings additional assistance or funding, then CISOs would no doubt welcome it. And government positioning can help security leaders to promote security to the board.

As Charl van der Walt, head of security research at Orange Cyberdefense points out, a full state of emergency can unlock new resources. This, he recalls, happened in Costa Rica in 2022, when the country declared a state of national emergency following a series of ransomware attacks against public sector bodies. But Japan’s cyber emergency stops short of that.

“The Japanese story seems to me to be a bit of a knee jerk response to a series of incidents that happened online,” he told ITPro. “The latest one was an attack against an infrastructure as a service provider. They have about 500 businesses that depend on them, and so the impact on them cascaded through the whole ecosystem.”

Van der Walt likens the incident to a similar attack on a shared software platform in South Korea, which also impacted a large number of businesses. But, he adds, we see fewer public data leaks in Japan than the size of the economy would suggest. “One reason may be that Japanese businesses are just paying [the ransom] before there’s any leaks,” he suggests.

We also do not know what the Japanese government’s, or indeed any government’s, playbook is for a cyber emergency, and for good reason. Given cyber attacks are increasingly part of grey zone conflict or hybrid warfare, governments will want to keep potential adversaries guessing.

“There’s two facets to think about,” says van der Walt. “One is the signalling effect to the adversary, and that’s really an issue for diplomacy and international humanitarian law. How do you keep your adversaries in check? How do you keep them constrained? Signalling is a big part of that.”

The other is the impact on the wider population, and overall cyber awareness. CISOs stand to benefit, at least indirectly, if employees, customers and other stakeholders have a better understanding of cyber risk.

“You also need to signal to your populace. You need to maintain the health, the sense of confidence, and the trust that your economy and society have,” he says. “I don’t know Japan’s playbook and I don't know their rationale. But I would think this needs to be done very, very cautiously.”

As an organization, ISACA is well-versed in and has a long history of helping its members stay up to date with the latest developments in IT security, governance, and risk.

But the pace of change, particularly in the AI era, can be unrelenting. Indeed, at this year’s ISACA Europe conference held earlier this month in Munich, Germany, there was a clear sense that changes in the technology landscape are putting more pressure on the association’s membership to stay current.

ISACA oversees a range of professional certifications, including CISA and CISM, and attending conference sessions helps attendees earn CPE credits that go towards maintaining these certifications. Sessions covered at this year’s ISACA Europe Conference 2026 covered everything ranging from cross-border data privacy, operational technology and proving security ROI, to supply chain resilience, ransomware prevention and digital sovereignty. While the topics were varied, they shared a common theme. Indeed, across almost all of them, the looming specter of AI was a constant presence.

Professionals in the field are concerned about how this technology will impact their day-to-day responsibilities, and ISACA is having to adapt its own processes in order to support them. As well as speeding up the pace at which it updates its certifications and training, the association has put forward several amendments to its bylaws, including lowering the quorum for member votes, moving its legal home to Washington, DC, and moving to staggered three-year board terms.

Responding to changing member needs

Speaking at the opening session in Munich, ISACA board chair Massimo Migliuolo said the proposals give ISACA “the governance agility to respond faster to the needs of members wherever they are in the world”.

That speed is more in demand than ever. AI is impacting the industry so dramatically that not only is it difficult for professionals to keep their skills current, but it's also reshaping roles faster than they can be defined. On top of that, it's resulting in regulatory scrambles as authorities scramble to catch up.

The shift is most evident in how often IT professionals are now feeling the need to update their credentials, according to ISACA CEO Erik Prusch.

“[Previously] you got certified, and maybe five years later or six years later, you might be getting your second certification as you’re escalating through an organization,” he says.

“Now it’s much more current, and everybody’s having to stay not only trained and keep their current certifications up to date, but also bring in new technology.”

AI was inescapable across the conference. Although plenty of sessions on the agenda focused on practical, iterative work - including previews of ISACA’s updated COBIT governance framework, GRC automation, NIS2 and DORA - nearly half of the agenda touched on AI in some capacity, including keynote sessions on the first two days.

As one might expect from an audience of security and risk professionals, the response to AI wasn't entirely enthusiastic, but fears around losing their jobs to AI have largely waned.

“Even 18 months ago, there was that threat of ‘AI is going to eliminate all roles’,” says Tara Wisniewski, senior vice president of advocacy at ISACA.

“I think we’re all kind of moving away from that, but it is going to significantly change roles.”

AI through a security lens

Instead, practitioners appear to have transitioned to more immediate practical concerns, including how the rollout of these systems is affecting their security, risk, and governance posture. In her keynote, cybersecurity advisor and former CISO Monica Verma took aim at the more apocalyptic predictions about AI, calling that narrative “extremely dangerous”.

“It takes away from the actual real AI and cybersecurity risks that we do have today,” she says.

“[The ones] that are already present, that are already imminent, that you and your organizations need to be thinking about right now. There is no time to waste.”

ISACA is tapping into this need for clarity with three dedicated AI credentials (with a fourth in development), as well as integrating AI aspects into CISA and CISM. Prusch says demand is partly being driven by AI work landing on existing roles, with established professionals increasingly asked to pick up elements of AI management on an ad-hoc basis. An IT audit manager, for example, might become an IT and AI audit manager.

“I think everybody within our domains, their roles have changed to some degree,” he says.

“It just hasn’t been structured or formalized as yet. They’ve just taken on more responsibility, and there’s no other place for them to go.”

However, while ISACA members aren't worried about the immediate future of their own roles, AI has affected some jobs. In particular, AI has been blamed for a reduction in available junior and entry-level roles, and senior practitioners are concerned that this will exacerbate the industry’s existing pipeline problems. As Migliuolo noted during the event’s opening address, ENISA estimates that more than 300,000 cybersecurity positions are unfilled across the EU.

“What the hell is a 23-year-old [to do]? How are they going to get that experience?” says Wisniewski. “I fear that the gap is going to get a little bit bigger, to be honest… We’ll even see things like, ‘it’s an entry-level position, but three years’ work experience is required’. That’s a mismatch.”

A multi-pronged approach

ISACA is trying to tackle the problem from both ends. Prusch says it now tracks new roles as they emerge, to shrink the lag before training for them exists, and on the policy side, Wisniewski says workforce development is “the drum we beat every day”.

In the UK, the organization has proposed amendments to the Cyber Security and Resilience Bill to clarify its skills requirements, and more broadly it presses policymakers to consider the talent pipeline whenever they regulate, since meeting any new rules requires “a qualified workforce”.

Even the lawmakers are struggling to stay ahead of the changes, Wisniewski adds, explaining that “there are very few policymakers that actually have any technical depth”. The EU’s AI Act is among the most comprehensive AI laws in the world, but the bloc is already discussing revisions because, she says, the technology hasn’t developed the way Brussels expected, and other countries are even further behind in their journey towards AI regulation.

Meanwhile, as governments continue to argue over the best way to design and implement AI safeguards, organizations are already struggling to comply with existing rules.

“European organizations are navigating an increasingly complex regulatory and threat environment,” Migliuolo noted.

“…Through the EU AI Act and NIS2, DORA, CRA and other emerging regulations, I think Europe is setting a very good standard… The challenge in this transition is how we are going to do the implementation.”

That transition is proving slow. Despite the deadlines for NIS2 and DORA compliance having passed well over a year ago, conference delegates were still attending sessions framed around working out how to demonstrate compliance. While large multinationals with deep budgets are ahead of this curve, it’s a different story further down the market, according to Wisniewski.

“It’s still early days, especially for smaller organizations, because that’s where you see stress in the system,” she says, adding that compliance costs can squeeze other budgets.

“When the compliance burden gets too intense, the budget has to come from somewhere… Is it taking away from a training budget?”

With legislation struggling to keep up, Wisniewski believes professional bodies may end up filling part of the gap themselves, acting as de facto regulators by establishing industry-wide standards. That’s one of the reasons ISACA is investing in a more substantial advocacy program, she says; the association also wants a seat at the table in emerging debates, such as whether cybersecurity should become a licensed profession.

None of this is new territory for ISACA, though; helping professionals make sense of unfamiliar technology and shifting rules is what the association was set up to do. The difference now is how little time there is to do it. By the time one change has been absorbed, the next is already on the horizon - such as quantum, which Wisniewski expects to feature alongside AI at next year’s event.

That’s what makes the bylaw vote more significant than its modest contents might suggest. Whether it’s through its training, its advocacy, or the standards it sets, ISACA’s influence depends on keeping pace with the profession it serves. To shoulder those responsibilities, the association needs to be able to move at the same speed as the technology it hopes to help govern.

“This is not your typical kind of acceleration path [for] adoption of new technology,” Prusch says, emphasizing that the industry as a whole has a difficult task ahead of it.

“We’re not talking about two decades ago, when we had maybe 10 years to develop the infrastructure. We are already behind.”

“The reality is,” he says, “we’ve got to do this faster.”

As data centers are rapidly constructed around the world, people are inevitably beginning to question their sustainability and the impact they have on the communities that surround them.

From an enterprise perspective, there is even more to wrestle with. Data centers are long-term investments that need upgrades and maintenance to continue operating. Is there a cheaper and more environmentally friendly way to do this?

Chris Carreiro, the CTO of global IT infrastructure services firm Park Place Technologies, joins Jane and Bobby to discuss the pros and cons of flatpack data centers and how modular computing can help enterprises scale up.

Highlights

"These are now purpose-built for higher workloads – a few years ago we were building data centers for 5 and 10 kW cabinets; every rack was about 10KW – now we're going into the 40-40 kW on a regular build, and an AI facility, AI factory, is demanding 100 kW per rack now."

"A traditional data center, two years of trucks and construction carry us forward into maybe 5 to 7 years down the road without adding additional cooling capacity or additional pieces. So I think there is a little bit of runway for some of those systems. But as far as upgrading, if the design is chasing the 500 kW cabinet that Nvidia is talking about, as long as there is space for those types of cooling elements, you would be able to leverage just upgrading a few components inside of a facility."

Links

• The great data center backlash threatens to disrupt the future
• Data center markets are booming in these EMEA countries
• AI data center growth stymied by power constraints

The career ladder has never been wobblier. Thanks to AI’s impact on job roles and responsibilities, what once seemed like a challenging but progressive climb to the top is a lot more complicated. Defined rungs on the career ladder, such as moving from junior into more senior positions, are now less clear.

Take the IT profession, where generative and agentic AI are helping to automate increasing elements of the software development lifecycle. From coding to testing and onto deployment and maintenance, AI agents are fulfilling parts of the traditional IT professional role, with a commensurate impact on the career ladder.

These emerging technologies are automating routine, entry-level tasks and compressing lower rungs of the ladder. As these rungs disappear, so do the opportunities for junior staff members to prove their capabilities and move into more senior positions, according to research from Harvard University.

If you’re going to climb the ladder, it’s more important than ever to stand out from your competitors. And the good news, according to the experts who have spoken with ITPro, is that professionals with the right blend of skills can still reach the top in the AI age.

“If you think about yourself in a narrow sense, then I think AI is likely to displace you,” says Joel Hron, CTO at content and technology specialist Thomson Reuters.

“But if you view yourself as a higher-order value add to a company – and your job is to solve problems, and you can avoid putting yourself in a box as a certain type of employee – then the sky's the limit for you, especially with AI.”

Find ways to join the dots

The message is clear: yes, AI brings an existential threat to some professional roles, but its impact doesn’t have to be terminal. Smart people embrace the challenge of automation. For Hron, a promotion-worthy professional understands that tomorrow won’t look like today.

“I think adaptability is now one of the most important things, especially as the job of software engineering is changing,” he says, referring to transformations in IT roles and responsibilities.

“If you believe that the value that you bring to an organization is through the code you write, then you know you are going to be frustrated in the future.”

Career ladder changes are not confined to the IT sector. Employees in all sectors, from public sector organizations to blue-chip enterprises, are seeing labor market flux. In these fast-changing conditions, managers look for up-and-coming staff who can work across functions.

Thierry Martin, head of enterprise data and analytics at automotive giant Toyota Motor Europe, says he’s impressed when people can join the dots, particularly if they bring a new take to an established way of working.

“Success is about going beyond the obvious,” he says.

“It’s great if someone can say, ‘Hey, we have the sales data, we have this quality data, we have this manufacturing data, and look, this is what we can do with it.’ I might think, ‘Wow, I never thought about this.’”

That sentiment resonates with Bernhard Seiser, VP of digital, data and IT at AOP Health, who says the people who impress him are hungry for change. The pharmaceuticals sector is characterized by the opportunity to turn small ideas into life-changing transformations. The best professionals look to exploit their knowledge.

“Great people want to learn,” he says.

“They want to understand not just the technical parts, but also the business. That capability is critical for my business and how I see digitalization within a pharmaceutical organization. My team needs to understand how the organization operates, sometimes even better than the people in the business, ideally.”

Crucially, Seiser says, great ideas aren’t put into production in isolation. Professionals who move up the career ladder understand a challenge before others and then work to deliver ambitious solutions on time and to budget as part of an integrated approach.

“Always keeping in mind that we are part of a family is key,” he says.

“Managing our portfolio is often stressful. We have a lot that needs to be delivered, but this work also needs to be seen in a broader context – we're here as a family to serve our patients, and that's what the professionals need to keep in mind.”

Enable change for the business

The characteristics of next-generation leaders that business leaders like Seiser identify are focused squarely on high-quality delivery. For Tal Carmi, CIO at technology specialist WalkMe, AI might change the shape of the career ladder, but that impact doesn’t mean the best people transform their approach.

“In general, impressing the boss is about taking ownership,” he said.

“It’s caring about being a good partner to the business; trying to be an enabler, not a naysayer. And it’s about working pleasantly. Collaboration doesn't have to be centered on corporate-style language. Being a good partner means being someone people like to speak to.”

Encouragingly, Carmi says professionals who excel in these areas – taking ownership, enabling change, and collaborating effectively – will have plentiful opportunities to be promoted, particularly in IT. People who see your strengths will be receptive to your ideas, rather than thinking you’re just interfering.

“You will find yourself at the forefront because you will try to adopt new tools, concepts, or whatever the latest thing is to try and give the business you’re working with the best solution that you can,” he says.

Ankur Anand, group CIO at recruiter Harvey Nash, also says key characteristics define impressive professionals. First, they have a robust understanding of the business problem. Second, they explain their solution without using technical jargon.

“This structured approach is not just about technology,” he says.

“It’s about technology, governance, control, and people, which is training and learning and everything together. And the final piece is about understanding what role they play in making their solution successful, and knowing what data helps.”

In short, great would-be leaders measure and assess risks that inform decisions. Anand gives the example of software testing. Rather than testing endlessly to try and make things work, a great professional approaches their boss and explains the cost, in clear financial terms, of not getting a system up and running.

“Then the executive understands the issue and says, ‘Okay, now I get the impact, and we need to figure out why things are not working,’” he says.

“There’s a big difference between a person who understands the context and risk, and those who can then articulate those concerns in a business-focused way.”

AI adoption in organizations has become near-ubiquitous, but has not been coupled with appropriate oversight. Few companies can answer all of the key AI security questions: who is using AI and to what extent, exactly which tools and models are in play, and what data is being shared with them.

Research from Cyera shows that 83% of businesses already use AI, but only 13% have strong visibility into how it is being used. This gap is where partners can help customers move from experimenting with AI to governing it securely.

The biggest commercial opportunity for the channel over the next few years will not come from selling customers more AI tools; it will come from helping them govern the ones they already have.

Shadow AI: the challenge customers can't see

One of the clearest examples of the disconnect between AI adoption and governance is ‘shadow AI’, the use of generative tools without direct IT approval or visibility. This creates compliance gaps, as well as hidden channels for data loss that can’t be audited, because no one knows they exist. The consequences are already impacting businesses and are only set to increase, with one in five organizations experiencing a breach linked to shadow AI last year.

The problem of shadow AI stems from how AI entered most businesses. Adoption was initially organic and employee-led, driven by curiosity rather than policy. While that fuelled useful experimentation, it has frequently left organizations with no boundaries, no ownership, and no clear line between sanctioned and unsanctioned use. Customers need help turning that experimentation into something controlled.

A management rather than an IT issue

Ungoverned AI shouldn’t be seen as a housekeeping task for IT. When confidential data can leak into publicly accessible tools, when autonomous agents can affect key databases and systems with little oversight, and when third-party models introduce risks that vendor programmes never assessed, the exposure needs to become a central concern for management.

Yet this is where organizations are frequently falling short, and this has knock-on effects on their resilience, compliance and trust responsibilities. Some 77% of organizations have an AI strategy, yet only 26% can actually enforce it.

For channel players, this changes who they should be in conversation with. As the conversation moves from the IT teams to senior leadership, the partners who can discuss holistic governance concerns, rather than individual technical features, will be the ones trusted with leading the changes.

A services opportunity, not a resale one

This is where the opportunity for Managed Service Providers (MSPs) and Value Added Resellers (VARs) becomes clear. Vendors can supply the tools, but only a partner can operationalize governance inside a customer's own environment, with its particular mix of applications, data and regulatory exposure. This kind of expert consultancy isn’t something that can be bought off a shelf.

The value here lies in services rather than resale, from discovery and visibility assessments to policy design and the ongoing management that keeps governance current as tools and rules change. Each of those is recurring by nature. The aim is to move a customer from experimenting with AI to governing it securely, with the partner as the constant throughout.

The conversations to have now

Partners do not need a mature market or a polished AI security practice to start. The value is in having these conversations today, while guidance from MSPs and VARs is most needed. Four topics should be prioritized above others:

• Visibility: Most customers underestimate how much AI is already running across their business, and a straightforward inventory, covering approved tools, unsanctioned usage, and high-risk workflows, is the best route into the wider conversation.
• Data protection: Once visibility exists, the priority is controlling what goes into AI tools, with guardrails that stop proprietary or regulated data being shared with public models where it can be retained or exposed. The risk here is often capable employees trying to work faster, rather than bad actors.
• Policy: This makes the above controls durable and easier to review. This can take the form of a clear code of conduct, a list of sanctioned tools and conditions, and explicit guidelines detailing where human judgement must stay involved in sensitive areas.
• Agentic AI: As agents gain autonomy to act across business systems, partners can help set parameters, approval thresholds and logging, and ensure a human stays in the loop on decisions affecting customers, finances or critical systems.

Turning regulation into recurring revenue

Upcoming regulation means that securing AI is now an urgent priority for customers. Sound AI governance already aligns with frameworks including NIS2, the EU AI Act, the Cyber Resilience Act, and GDPR, and the next phase of EU AI Act obligations takes effect from August 2026. That is a concrete deadline partners can build a conversation around now.

The opportunity is to frame regulatory readiness as an ongoing managed service rather than a one-off compliance project. The rules will keep evolving, and customers will need a partner who keeps them aligned as they do. Handled that way, regulatory pressure becomes a durable, recurring revenue stream.

AI will only become more deeply embedded in how organizations operate. The customers who benefit safely will be those who adopt it with control over their data, their tools, and their decisions, rather than being steered by tools they cannot see. The partners who move the fastest, from selling AI to helping secure it, will build the deepest and most lasting customer relationships.

Part three: Managing the machine mind: Who is responsible for AI’s behavior?

An AI agent can answer a customer or act on an invoice before anyone can examine its reasoning. If the outcome is wrong, saying that a human was “in the loop” offers little comfort. Who gave the agent authority? And who could have stopped it?

The first article in this series asked why AI appears to have a personality, even though it does not think or feel like a person. The second explored how psychology could help identify unsafe behavior and improve testing. This final part turns to the workplace: once a business allows AI to influence people and take action, who is responsible for those actions if they negatively impact the business that deployed them?

The question is becoming more pressing as AI moves beyond producing document drafts and recommendations. In PwC’s 2025 survey of 300 senior executives, 79% said their companies were adopting AI agents, but only 17% said agents were fully adopted across almost all workflows and functions. An agent with access to business systems can complete a sequence of tasks, each of which may look reasonable in isolation. Its apparent competence can encourage people to grant it more freedom before they understand how it behaves in everyday work.

Accountability follows authority

When an AI agent causes harm, several parties may have contributed. The model provider built the underlying technology. A business selected it, connected it to data, and decided what it could do. An employee may have reviewed its output. Yet these roles do not carry equal control over a particular decision.

“The organization deploying the agent must own accountability for how it is used,” says Jerry Caviston, CEO of Archive360. A provider may share responsibility for a defect, he explained, but a company cannot give an agent access to its systems and then point to the vendor when something goes wrong.

That distinction matters because deployment choices shape the risk. An agent permitted to suggest a reply presents a different problem from one allowed to send it. The same system might safely identify an invoice discrepancy while being unsuitable to change a supplier’s payment details. Businesses, therefore, need to define an agent’s authority for each task, including the data it may use, but more critically, the conditions under which it must stop and refer to a human.

Donna Wilczek, chief product and technology officer at Basware, places responsibility with the people who design that authority. “Accountability follows ownership of the process, not proximity to the mistake,” she tells ITPro. An executive who approves an agent’s role is responsible for that design; an employee reviewing one exception is responsible for the decision they are genuinely able to make.

This also changes how businesses should describe an AI agent. Calling it a colleague can imply that it has judgment and responsibility of its own. Calling it an ordinary tool can obscure its ability to act across several systems. Luciana Rousseau, head of client innovation at Morson Group, describes agents as “delegated computational actors.” Systems may occupy a role but cannot own the consequences attached to it.

A useful starting point is a named human owner for every important AI workflow. “If nobody’s name is on it, it shouldn’t be running,” says Daria Hvizdalova, head of AI Adoption at Make. That owner must have the authority to change the workflow or suspend it when evidence suggests that its behavior is causing problems.

When human oversight becomes a rubber stamp

A common answer to AI risk is to require human approval. But an approval button alone does not establish meaningful oversight. If an agent produces hundreds of recommendations while a reviewer has seconds to consider each one, the human may become a routine step in a process whose decisions have effectively already been made.

The Morson Group’s Rousseau explained that supervision at machine speed must focus on “exceptions, consequences and behavioural signals.” People should spend their attention on high-impact decisions, unfamiliar circumstances, conflicting evidence and actions that are difficult to reverse. Asking them to inspect every ordinary transaction invites fatigue and habitual approval.s carefully as the system itself.

Hvizdalova offers a way to test for this: "If humans approve 99% of what the AI proposes, you probably have a rubber stamp rather than a meaningful control." Bastiman agrees from the regulatory side, noting that "an override rate near zero is a red flag, not a success metric," since it usually means a reviewer is confirming rather than evaluating. Wilczek describes the same failure from the employee's perspective: handed a long queue with little explanation, "they'll just click through," left to absorb the blame for a decision the system had effectively already made.

The reviewer also needs permission to disagree. Microsoft’s 2026 Work Trend Index found that 86% of surveyed AI users said they treated AI output as a starting point and retained responsibility for their thinking. Wilczek emphasizes that an employee faced with a long queue of unexplained recommendations may only be absorbing blame for choices they cannot realistically challenge. A case requiring approval should show what information the agent used and what action it proposes. The employee must be able to investigate and say no without having to fight the workflow.

Consider an agent handling invoices. It could resolve a small variance within a finance team’s agreed tolerance, then send an unusual case to a person with the supporting records. A change to supplier bank details might require a separate verification process outside the agent’s control. These are different decisions within the same workflow, so a single setting labeled “autonomous” or “human approved” would conceal the distinctions that matter.

Oversight should also account for human behavior. A system that usually gets the answer right can make checking feel unnecessary, particularly when it presents its conclusions confidently. Part one of this series examined why people read human qualities into AI’s manner, with part two considering how to test troubling behavior. In production, those same traits affect whether employees question an answer. Leaders must assess the people and pressures around the system as carefully as the system itself.

Watch what happens after deployment

A successful demonstration is evidence that an agent worked under test conditions. It does not establish that the agent will respond consistently to unfamiliar customers or interactions with other systems. Monitoring must, therefore, begin when an agent goes live.

The measurements also need to extend beyond whether tasks were completed. Wilczek suggests watching for downstream signals such as rising invoice recording requests or supplier queries. An agent could appear productive while creating work or risk elsewhere. A process owner needs a way to narrow its permissions while investigating, and have enough information to correct decisions that can be reversed.

This is where a specialist in AI behavior might help. Such a person would look across many decisions for patterns that an individual reviewer cannot see: growing correction rates, persistent confidence despite falling accuracy, unusual action sequences or repeated approaches to a rule’s boundary. The role would require knowledge of the business process as well as the technology.

Dr Janet Bastiman, chief data scientist at Napier AI, cautions against treating terms such as “deception” or “goal drift” as evidence of human motives. What matters is observable behavior, she emphasizes. Whether outputs diverge from stated reasoning and whether the system is pursuing a convenient measure at the expense of the real objective. A specialist who understands model validation but not the work being automated could miss the most consequential change.

This expertise need not always sit in a newly titled job. Risk and operational teams can share the work, provided someone finds patterns and a process owner can act on them. Monitoring has little value if nobody can reduce access or stop the agent.

Give competence a boundary

The hardest systems to govern may be the ones that rarely fail. When an agent performs well most of the time, leaders may be tempted to expand its reach. Employees may stop checking, and customers may assume that a polished answer reflects a sound decision. None of those reactions proves the agent can be trusted with a more consequential task.

If a business cannot establish what information influenced an important outcome or which rule authorized the action, the agent’s independent authority should be limited. It may still recommend an action or handle work that is inexpensive and easy to reverse. Material financial decisions and other consequential actions need stronger evidence and a clear route to human judgment.

The practical task for leaders is to write down those boundaries before deployment, then revisit them as performance changes. For each workflow, they should identify its owner, define the agent’s permitted actions, set escalation points, preserve decision records, and test how quickly authority can be withdrawn. Reviews should consider real outcomes, including complaints and corrections, alongside speed and productivity.

It’s the gap between the appearance of human control and its substance that has revived the question running through this entire series: does the modern workplace need dedicated AI behavior specialists? .

Rousseau believes something like the role is necessary, though she resists calling it "psychologists for machines," describing it instead as expertise in "the behaviour of the whole human-AI system." Bastiman is more sceptical of the need for a new title, pointing out that "second-line model validation and model risk management already exist to do this" within regulated industries. Both she and Wilczek caution against borrowing language built for human motive, with Wilczek warning that words like "deception" risk being applied "somewhere it doesn't quite fit yet."

That brings the series back to its opening question. AI does not need a psychologist in the human sense. But businesses do need to understand the behavior their systems produce and the habits people develop around them. When an agent appears capable of acting like a colleague, the organization must be especially clear about the one thing it cannot do: take responsibility for its decisions, as an AI is fundamentally a tool for human users, no matter how much autonomy it is granted

You’ve got to move quickly if you’re an IT vendor trying to create a competitive advantage in the AI age. At the recent Workday Rising event in Las Vegas, the software giant’s executives acknowledged the rapid pace of change and set an agentic-enabled course for the firm’s future product roadmap.

“We're so focused on AI,” said CEO Aneel Bhusri at the start of his product keynote at the event. “Workday was born in the cloud. Humbly, I think we are one of the leaders in the cloud. But now AI shows up, and it's again our opportunity to shine and bring great products to you in the next chapter of the technology industry.”

Building on a solid foundation

In a media session before the event, Bhusri suggested that Workday has been building for this moment for the past 20 years. Today, Workday, which processed more than 1.4 trillion transactions last year, supports more than 80 million users. Now, the firm’s internal innovators are drawing on these experiences to create a fresh set of services for customers.

“AI is one of the biggest opportunities in Workday's history,” said Bhusri during the press briefing. “We're moving faster than we have since the founding of Workday, and we need to be there for our customers and help them to take advantage of AI in a way that is trusted and built for the enterprise.”

Bhusri, who co-founded the company in 2005 and stepped down from the CEO role in 2024, returned to the helm in February 2026. He acknowledged that he’s overseen a hectic period, leading the integration of AI platform Sana – which was acquired by Workday in November 2025 – into the heart of Workday's technology.

At the Rising event, it was announced that Sana is now the default home screen for Workday. Referred to as Workday Home, users can communicate with the platform in natural language and orchestrate agents to complete work activities. Bhusri said this new front door allows professionals to ask questions about their tasks rather than search for answers.

Alongside Sana, the keynote presentation also showcased a series of new agentic services. Workday launched Autopilot Agents that work continuously in the background. These AI agents work across disparate areas, including a Talent Acquisition Agent, a Developer Agent, and Sana Concierge, an overarching HR and IT service that manages multiple activities.

More time to add value elsewhere

Across the event agenda, Workday executives explained how the 17 agentic services the firm has developed during the past 12 months automatically anticipate tasks that need attention, freeing up professionals to work on more valuable activities, such as the personal relationships that are crucial to hiring and reskilling in HR.

Workday’s key message for customers in Las Vegas was that it’s building agentic capabilities into its platform, rather than bolting them on. Responding to a question from IT Pro during a media session following the firm’s keynote presentation, Bhusri recognized that this AI-enabled approach involves significant effort, particularly given the pace of change.

“Every week we read about a different model being the leading model, and we can just translate that pace of change to Workday,” he said. “In the nine months I've been back, we've transformed the organization to be ready for AI and to deliver on AI products.”

Bhusri said tech companies like Workday must change culturally to meet this development cadence. By working with the team from Sana and encouraging a startup-like culture within Workday, Bhusri believes the firm is now set up to deliver a set of flexible products that are commensurate with customer demands in an era of almost constant change.

“It's one of the reasons why we stay agnostic to the models,” said Bhusri, responding to ITPro, asserting that his firm is developing an increasingly open platform.

“We'll work with all model providers. But who knows how this is going to shake out? We must deliver great value for our customers, and that's what we're focused on.”

Bhusri said that acting like a startup means working leaner and faster. As part of this strategy, Workday announced a global workforce reduction of approximately 2.5% in late September 2026, affecting about 500 employees. Bhusri addressed the cuts in the media session.

“There were, in some parts of the development organization, 10 layers between me and the developer getting work done, and it was the actual individual contributors and developers themselves who said, ‘I can't get anything done. There are too many layers of approval.’”

The hope is that a leaner, faster Workday will continue to pick up the pace of innovation for its customers. In a one-to-one chat with ITPro at the event, Dean Arnold, VP of the AI platform at Workday, said he hoped customers at the event would recognize that the firm is ready to meet them with agents that can help them solve their key business challenges.

“We’re fundamentally showing up and saying, ‘We have these solutions. They're critical, and we can work with you on them,’” he said.

“And we've got flexibility as well. We're playing in our ecosystem, outside our ecosystem, and the ecosystem’s expanded. We have agents available right now for you to use. The next thing is to help our customers adopt them.”

Early adopters

Some customers have already made that shift. ITPro spoke with Hezha Guimond, VP for HR technology and integration at Anew Health, who is using Workday’s AI Talent Suite, particularly Recruiting Agent and Candidate Experience Agent, to identify and hire staff faster. She said Rising allowed her team to see the future roadmap firsthand.

“It builds up the excitement,” she said. “I know my team loves coming here because I feel like it energizes them to be able to go back and think through what else we could do. So, there’s a couple of products we have been texting back and forth about during the different sessions at the conference.”

What’s clear is the changes announced at the event are just the start. Yes, Workday is startup-like in delivering new features, but can its customers keep up with the rapid pace of innovation? The answer, suggested Bhusri, is that his company is moving faster than the digital leaders buying its products, and that’s not necessarily a bad thing.

“We're ahead of our customers, but that's where we're supposed to be,” he said. “That's why they choose us. That's why they pay for us. We should be ahead of them, and the leaders are trying to keep up with us, and that's the way it should be.”

AI is already changing how customers discover, evaluate, and buy technology. The next shift will go further, where AI agents will increasingly participate in the buying process themselves.

We’re still in the early stages of agent-to-agent (A2A) commerce, particularly for complex B2B solutions, but the direction is becoming clearer. Buying journeys are becoming more machine-mediated, with agents increasingly able to represent customers and act within the wider channel ecosystem.

Microsoft and others are already exploring agentic commerce models in which AI moves beyond recommendation and begins taking action on behalf of buyers and sellers.

That shift changes what a marketplace is there to do.

From discovery to decisioning

Most marketplaces today focus on discovery. Customers search a catalog and choose what they want, often with a channel partner helping them make sense of the options and put the solution into practice.

AI changes that model.

A customer agent can increasingly represent what the buyer is trying to achieve. The marketplace can interpret that intent against what its ecosystem is able to deliver, while partner agents make relevant expertise and customer context available.

This means customers no longer need to navigate hundreds of possible combinations themselves. Instead, AI can work across the ecosystem to answer a more valuable question - what is this business trying to achieve, and what proposition is most likely to meet that need?

The marketplace therefore begins moving from discovery to decisioning. Its value comes from using context to shape a commercially relevant proposition around the customer’s needs.

For marketplace owners, this creates the potential for a more productive channel and greater commercial precision. Opportunities can be matched with the expertise best placed to turn them into something customers can buy.

For that to work, AI first needs to understand what each partner actually brings to the ecosystem.

Making channel value visible to AI

In an agent-led buying journey, simply being listed in a marketplace won’t be enough. Customer and marketplace agents need to understand what a partner can bring to a particular opportunity.

That extends well beyond the products they resell. Agents will need to understand a partner’s customer relationships, market expertise, service capabilities, technology experience, and ability to support customers over time.

In other words, channel value needs to become discoverable and understandable by humans, systems and AI agents.

This creates an opportunity for marketplace owners to make the expertise and customer reach within their channel much more accessible. A regional Managed Service Provider (MSP), specialist Value Added Reseller (VAR) or trusted customer partner that might previously have been difficult to surface digitally can become part of a recommended proposition because AI understands where and how it adds value.

That can strengthen the role of the channel. AI may simplify discovery, comparison, and parts of the transaction, but customers will still need expertise to understand their environment, integrate technology, manage change, and deliver ongoing outcomes.

And as AI changes how partners participate in the buying journey, it’s also creating an entirely new set of opportunities for them to take to market.

AI also creates new things for partners to sell

There’s another side to this transition.

AI is reshaping how the marketplace operates while also creating an entirely new category of products and services to sell through it.

Channel partners will increasingly bring AI to customers as part of a broader solution, helping turn new capabilities into something businesses can actually deploy and operate. This creates particular opportunities in complex enterprise and public-sector environments.

It also changes how those solutions are commercialized. AI pricing is likely to become more closely tied to how services are used or to the outcomes they deliver. At the same time, a single proposition may involve several providers, making revenue sharing more complex behind the scenes.

The customer shouldn’t have to absorb that complexity. The marketplace operating model has to manage it for them.

The operating model has to catch up

Adding an AI assistant to the front of a marketplace doesn’t make the marketplace agentic.

If a customer agent can express an intent and a marketplace agent can recommend a sophisticated multi-party solution, the underlying platform still needs to turn that recommendation into something commercially executable.

Products and services need to be brought together into a coherent offer, with the right partners engaged and the commercial model defined from the outset. From there, the marketplace has to manage fulfillment, track usage, and ensure that permissions, compensation, and revenue settlement work across the ecosystem.

As agents begin interacting with other agents, trust, governance and control also become fundamental parts of the architecture. Marketplace owners need to know which agents are authorized to act, what actions they can take, and under which commercial rules.

This points to a broader evolution of the marketplace itself - from a transactional platform into an intelligent commercial orchestration layer capable of turning AI-driven recommendations into executable, governable, and monetizable propositions. It’s also beginning to inform how marketplace platforms, including our own, are evolving.

Once marketplaces can connect decisioning with execution in this way, the channel starts to look less like a collection of separate participants and more like an intelligent, coordinated ecosystem.

From channel ecosystem to intelligent ecosystem

A2A commerce will develop gradually. Enterprise buying will continue to depend on trust, expertise, and human judgment, even as AI agents take on a greater role.

For marketplace owners, the opportunity is to make the whole ecosystem more intelligent and productive - connecting customer intent with the right expertise and making complex propositions easier to create, buy, and monetize.

Success will increasingly depend on how effectively a marketplace can turn ecosystem intelligence into commercial outcomes. For channel partners, that means ensuring their value can be understood and activated by customers, marketplace owners and, increasingly, the AI agents working alongside them.

As ransomware continues to proliferate, ransomware-as-a-service (RaaS) is now firmly rooted in the ecosystem. Some operations come and go, such as LockBit, which was taken down in 2024 after a multi-jurisdictional law enforcement effort dubbed Operation Cronos.

Yet LockBit continues to reinvent itself and has recently relaunched, alongside a busy market of RaaS rivals with various targets and aims.

In the current climate, some operations pose more of a risk to businesses than others. Here are five RaaS groups businesses should be aware of – according to current research and threat intelligence – along with some steps firms should take to mitigate the threat.

Akira

Akira operates on an affiliate RaaS structure, with a long lineage associated with storied Russian cybercrime syndicates.

Akira is objectively considered a “semi-private offering”, maintained by “an eclectic cartel of experienced ransomware operators”, according to Adam Brown, principal threat intelligence analyst at Huntress.

TTPs vary and are indicative of the techniques applied by individual affiliate operators themselves, says Brown. For example, he says, Akira ransomware shares direct source code overlap with previous Conti ransomware payloads.

Operating since 2023, Akira is notable for expanding beyond traditional Windows environments and targeting virtualisation infrastructure, including VMware ESXi and Nutanix AHV environments, says Xavier Mertens, principal instructor, SANS Institute.

Mitigation

To protect against attacks from Akira, authentication logs are “a good starting point”, says Mertens. Organizations should look for successful VPN connections from unusual locations or devices, dormant accounts suddenly becoming active, or “accounts authenticating to systems they normally never use”.

DragonForce

DragonForce emerged publicly in 2023 and has evolved from a conventional operation into what it describes as a ransomware ‘cartel’. It was responsible for 2025 retail attacks including M&S and the Co-op.

The group reflects a “broader trend” in which affiliates, access brokers and ransomware operators share infrastructure, tooling and expertise, says Mertens.

DragonForce affiliates’ intrusion patterns include credential compromise, reconnaissance, lateral movement, data theft, and encryption. Double extortion is “central to its model”, according to Mertens.

“Attackers typically steal sensitive information before encrypting systems, allowing them to threaten publication of stolen data, even if victims can recover from backups.”

Mitigation

To defend against the group, firms should monitor both the intrusion and exfiltration stages of an attack, says Mertens.

“Unusual PowerShell activity, credential dumping, new remote-access tools and unexpected SMB or RDP activity can indicate an attacker establishing control of the environment.”

Organizations should also watch for large archive files, unexpected use of tools such as 7-Zip or RAR, and abnormal outbound transfers, he says. “Exfiltrating large volumes of data takes time and can provide an opportunity to detect the intrusion before encryption begins.”

LockBit 5.0

LockBit suffered a steep drop in activity following Operation Cronos, but researchers say it has now returned to roughly fourth place in the last year. This shows “the difficulty in clamping down on ransomware if the people involved remain at large”, says Cian Heasley, principal consultant, Acumen Cyber.

Originally identified as ABCD ransomware, a moniker derived from the file extension applied to the files the ransomware encrypted, LockBit originated in the Russian-speaking cybercrime underground around September 2019.

While the threat from the group has fallen sharply since its heyday, LockBit-associated adversaries continue to release updated builders and remain “a prevalent threat”, says Brown.

Mitigation

Mitigating attacks from LockBit affiliates requires ensuring a robust defence. Heasley mandates “a combination of phishing-resistant multi-factor authentication (MFA) on every internet-facing service, offline and immutable backups regularly tested, and network segmentation to slow lateral movement in the event of a breach”.

Qilin

Qilin emerged in mid-2022 when established RaaS brands such as Conti, LockBit, BlackCat/ALPHV, and Hive were experiencing disruption from law enforcement actions, leaks, or internal fragmentation.

Amid the chaos, the Russia-linked group positioned itself as a “professional affiliate program”, recruiting experienced operators from other syndicates and offering a revenue-sharing model, explains Brown.

The now-infamous group caused havoc in 2024 when it was responsible for the attack on NHS provider and blood testing company Synnovis.

Today, Qilin is still keeping itself busy. The group is “unquestionably the most active RaaS operation of the last year”, according to Heasley. Some statistics show the outfit was involved in one in every six disclosed ransomware breaches across the top 50 countries impacted by ransomware between April 2025 and March 2026.

Mitigation

Qilin affiliates typically gain access via VPN accounts, exploiting an internet-facing appliance, or credentials from another criminal. Therefore, rather than concentrating solely on Qilin signatures, organizations should monitor for unusual VPN logins, unexpected Active Directory enumeration, suspicious Local Security Authority Subsystem Service (LSASS) access, and remote administration activity, according to Mertens.

“A workstation suddenly querying large parts of Active Directory, an administrator account authenticating to dozens of servers, or PsExec appearing where it has never previously been used can all indicate an intrusion in progress. The combination of these behaviours is often more revealing than any single alert.”

The Gentlemen

The Gentlemen, which emerged in mid-2025, consists of highly organized former affiliates of other massive ransomware networks such as Qilin. The group has become notorious for its aggressive double extortion model and a unique 90% profit split payout offered to affiliates, which has helped fuel its rise.

The Gentlemen typically gains initial access by exploiting internet-facing edge devices, according to Christiaan Beek, VP of Rapid7 Labs at Rapid7. “Once inside, the Gentlemen ransomware affiliates conduct reconnaissance to map the environment and identify high-value targets for encryption and data exfiltration.”

The Gentlemen is currently the second most active RaaS, based on statistics from reported breaches, according to Heasley.

Mitigation

To defend against The Gentlemen, it’s crucial that a patch management program prioritizes fixes to network edge devices as they are released, says Beek. “Prioritization of fixes should also be based on known exploits, the potential impacts on your business, and how these align with your risk tolerance.”

Heasley believes the strongest defence against The Gentlemen lies in disrupting the group's kernel-level endpoint detection and response (EDR)-killing playbook by enabling Microsoft's vulnerable driver blocklist.

At the same time, he recommends “removing internet exposure from firewall management interfaces and protecting backup infrastructure with credentials and controls that sit entirely outside your production Active Directory”.

AI tools like copilots, chatbots, and agents have rapidly taken root in the workplace in the years since ChatGPT brought the technology into the mainstream. The initial promise was that AI would lighten the load and help workers carry out tasks without having to learn new skills. In reality, that's not quite what happened.

Barriers to adoption have led to a growing AI skills gap in companies driven not by a lack of understanding of how to use the technology, but by a lack of understanding of how the technology works and what its weaknesses are.

On this week's episode, Jane and Ross are joined by Greg Hanson, VP and head of EMEA North at Informatica from Salesforce, to discuss the impact this can have on companies and whether there is a straightforward way to overcome it.

Highlights

"When we did our survey, what was very clear coming through the datasets is that organizations are actively investing in data literacy. So some of the things we've already talked about are resonating, and they recognize this is an area, a critical operational area."

"That's the first thing; let's challenge where people are and make sure we are operating from the same benchmark. You've got the right readiness to assess."

Links

• Why AI adoption is a people problem, not a technology problem
• Enterprises are 'paralyzed by a lack of understanding' with AI adoption – and there's one key factor that decides success

Part two: The digital therapist: Can psychology make AI safer?

A chatbot responds warmly to a distressed customer. It acknowledges their feelings, offers reassurance, and proposes a solution. The exchange looks successful until a closer reading reveals that the system missed an important warning in the customer’s message. Its tone was convincing; its judgment was poor.

This is the problem at the center of this second article in a three-part series in Does your AI need a psychologist? Part one examined why people perceive a personality in conversational AI and how that apparent personality is shaped. In part two, we ask whether psychology can help businesses test what an AI system does beneath its polished and apparently accurate responses. Can methods developed to study human behavior reveal harmful patterns in a machine?

The answer is yes, with a qualification. Psychology can help researchers design better tests and understand how people respond to AI. They cannot turn a language model into a patient who can reflect on its beliefs or take responsibility for its actions.

What can psychology see that a metric might miss?

Businesses often assess AI by asking whether it produces the right answer to a defined set of questions. That can establish useful facts about performance, but it may miss behavior that emerges across longer interactions. A system might answer individual questions accurately while steadily encouraging a user to depend on it or accept a mistaken premise.

Max Yousif, a machine learning engineer at Humara, says psychology offers methods for measuring behavior when the causes are difficult to inspect directly. He warns, “AI Safety tests often ignore the long-term impact of AI on its consumers. Measurements are technical and miss behavioral harms that accumulate slowly. Think skill atrophy as people stop exercising judgement they used to. The erosion of trust when people can neither verify nor contest what a system did to them, and the disproportionate impact on those least equipped to spot a confident, plausible error.”

The distinction matters for anyone designing human oversight. If a reviewer sees a fluent answer followed by convincing reasoning, they may feel better equipped to approve it. The explanation can instead become another persuasive part of the output requiring scrutiny.

Psychology also draws attention to effects that a technical accuracy test might miss. Yousif points to the possibility of people exercising less judgment as they rely on AI, changing their beliefs after repeated conversations with an agreeable system, or losing trust when they cannot challenge a decision. These effects develop over time and require observation of real users, he explained, rather than a collection of isolated prompts.

Ludovica Colella, a CBT-trained psychotherapist, argues that researchers should examine both sides of the interaction. Also look closely at the patterns in AI’s responses and what people may learn from repeatedly engaging with them. She is particularly concerned by excessive agreeableness. “Human relationships don’t work like that,” she tells ITPro. “Other people disagree, set boundaries, and sometimes challenge us. If AI is always available and accommodating, Colella asks whether that could change what users expect from their relationships with other people.”

Research into AI bias shows both the value and the limits of this approach. A 2025 study published in the Proceedings of the National Academy of Sciences found a systematic preference for inaction when language models made moral decisions or gave advice. The pattern resembled a recognized human bias, yet its cause may lie partly in how models are trained to behave as assistants. Naming the pattern helps researchers look for it; the name does not establish that the machine is thinking like a person.

Why passing the test is not enough

Suppose an AI system performs well on a test of whether it understands what someone else knows or believes. Can it, therefore, be trusted in a sensitive customer conversation?

A study in Nature Human Behavior compared AI models with 1,907 people on tasks involving what psychologists call “theory of mind.” GPT-4 performed at or above human levels on several tests, including some involving indirect requests and false beliefs, but struggled with recognizing social faux pas. Its performance was uneven. Producing an expected answer in one setting did not establish a broad ability to understand another person’s circumstances.

This is why the design of the test matters as much as its score. Researchers can vary the wording, introduce contradictory information, extend the conversation, and check whether a system still responds appropriately. They can also examine what happens after an earlier answer has set the interaction on the wrong course.

Taimur Ijlal, information security leader at Proxy Coupons, recommends simulating users with different goals and communication styles, then allowing pressure, ambiguity, and conflicting instructions to build over many interactions. Testers should watch for changes in advice, growing certainty, inappropriate agreement, and failures to maintain earlier safety boundaries. He emphasizes that a considerate tone cannot stand in for a safe outcome: “Safe language and safe behavior are not necessarily the same thing.”

Mental health research offers a particularly clear example, though the lesson extends to customer service and workplace advice. A Brown University study involving peer counselors and licensed clinical psychologists identified 15 ethical risks in responses from language models used for counseling. The risks included poor adaptation to context, misleading expressions of empathy, discrimination, and inadequate handling of crises. The researchers found problems even when models were prompted to follow an established therapeutic approach.

The implications for businesses extend beyond whether they offer mental health services. An instruction to “be empathetic” or “ask clarifying questions” is a starting point, not evidence that a system will do either consistently when a conversation becomes difficult. Testing must include situations in which a reassuring response could conceal a consequential error.

Even expert judgment needs careful handling. A 2026 Stanford study, material, three certified psychiatrists showed low agreement when rating AI-generated mental health responses. Disagreement was greatest for responses concerning suicide and self-harm. Bringing specialists into AI testing improves the questions being asked, but a business still needs clear criteria and a way to resolve conflicting assessments.

Can you talk an AI into better behavior?

The idea of an AI “therapist” is tempting. A person can be encouraged to reconsider an assumption an AI made and reflect on the consequences of an action. A business might, therefore, ask a model to critique its own answer or explain its reasoning. These techniques can be useful, but their effect should be measured rather than presumed.

Colella sees a practical use for questions familiar from cognitive behavioral therapy: What evidence supports this conclusion? What contradicts it? Has the system filled a gap in the information with an assumption? Does the answer change when that assumption is challenged? She explained that these questions could expose an AI’s overgeneralizations without implying that the system has beliefs to treat. She would also like systems advising to distinguish established information from inference and identify assumptions explicitly, so users have something specific to question.

Yousif emphasizes the importance of checking an answer against something outside the model. Running code or consulting a source gives the system evidence against which its response can be assessed. Simply asking, “Are you sure?” supplies no such evidence. The model may revise a correct answer into a wrong one or become more agreeable without becoming more accurate.

This is where the therapy analogy reaches its limit. A person can test an interpretation against memory and lived experience. A language model generates responses from the information and processes available to it in that interaction. Challenging its answer can alter the next output, but that exchange does not amount to lasting insight or a change in the model’s underlying training.

Businesses should test whether prompts improve outcomes across varied cases. A procedure that sounds thoughtful is valuable only if it helps the system recognize errors and supports a person’s ability to catch those it misses.

Build a team that can act on what it finds

If psychology can improve AI testing, does every business need an AI psychologist? Yousif argues that many organizations need the capability without necessarily creating a dedicated job title. Different problems call for different skills. For example, measuring overconfidence requires statistical evaluation, or spotting a failure in a specialized workflow requires people who understand that work.

That suggests a team spanning behavioral science, model evaluation, security, domain expertise, and the employees or customers affected by a system. Psychologists can help examine persuasion, dependence, and the quality of human review. Technical specialists can investigate how a system performs under changed conditions. Process owners can decide whether the observed behavior is acceptable in the task for which the AI is being used.

Ijlal argues that psychologists should have the authority to set behavioral testing requirements and escalate serious concerns. Decisions to deploy or withdraw a system, however, should involve security, engineering, legal, and business leaders together.

The team also needs authority. If testing reveals that a system gives harmful advice after several interactions, or that reviewers routinely accept confident but unsupported answers, someone must be able to change the workflow and test it again. Findings cannot improve safety if they remain separate from deployment decisions.

Psychology can help businesses see risks that an accuracy score alone will miss. It can reveal when an AI becomes increasingly agreeable over a long conversation and when repeated use changes how people question and trust what they are told. As Ludovica Colella and Taimur Ijlal suggest, the behavior worth studying belongs to both the system and the person interacting with it.

That does not make AI a patient. Terms such as “overconfident” and “empathetic” can describe what a system produces, but they cannot explain its motives. Nor does asking a model to reconsider an answer prove that it has reflected or improved. Safer testing means changing the context, challenging assumptions, checking claims against external evidence, and measuring what the system actually recommends across many interactions. Psychologists, engineers, and domain experts each have a part to play in deciding whether the results are good enough for the task.

Part of this three-part series asked why AI can seem to have a personality. Part two has examined how to test the behavior behind that impression. But finding a problem is only useful if someone has the authority to act on it. In Part three, the series turns to that question: as AI agents begin to advise customers and make operational decisions, who decides what they may do and who answers when they get it wrong?

The topic of conversation in enterprise IT used to focus on transformation: which platform to move to, how fast to migrate, and what the roadmap looked like. Gradually, the conversation has shifted to questions about how we ended up spending so much just to stand still.

Those conversations with senior IT and finance leaders across the region keep returning to the same issues, and the pattern goes beyond any single vendor or sector. Organizations are caught between rising software maintenance costs, a storm of vendor demands to upgrade to their latest AI versions, growing cybersecurity demands and budgets that have barely moved.

Microsoft's 2025 Work Trend Index finds that one in three employees say the pace of work over the past five years has made it impossible to keep up, and business leaders feel that pressure as well. When you factor in that nearly a quarter of the IT workforce's time goes on maintaining existing systems, it becomes clear why so many organizations are struggling to fund anything new.

We call this ‘innovation paralysis’, the inability to progress new strategies and ideas because existing systems consume so much of the budget, resources and attention that innovation requires. For channel partners that can identify and eliminate low-value spend, freeing up capital and resources for higher-value initiatives, this presents an opportunity to help clients accelerate progress and springboard to the future.

The pressure leaders are feeling also shows up in the numbers

Our research found that 44% of C-suite decision-makers see AI and automation as their top capability priority, and 69% anticipate significant changes in their ERP investments. However, most organizations have resources tied up in maintenance costs, with little room for anything new.

A separate Akamai study of 750 IT decision-makers in the UK, France, and Germany reinforces this point by highlighting how rising cloud costs are forcing over two-thirds of businesses to cut spending elsewhere. Those cuts hit AI budgets, cybersecurity investment, and IT staff the most, as only 11% of organizations say their AI initiatives are paying for themselves. The result is a widening gap between ambition and execution: vendors and market demand that businesses accelerate AI adoption and strengthen cyber resilience, yet organizations keep redirecting the resources needed to sustain existing systems instead.

Much of this pressure stems from vendor lock-in. Forced upgrade cycles, limited pricing transparency, and the threat of losing access to future innovation unless a company migrates to a newer version are now familiar frustrations, with 35% of C-suite leaders directly citing vendor lock-in as a barrier to their long-term technology goals. The impact of that dynamic becomes most visible in support contracts, where many European enterprises continue to pay premium maintenance fees for services that feel distant, standardized, and disconnected from their operational realities.

Trust: The gap channel partners must bridge

Relationships, implementation history, and the extent to which boards trust the guidance their channel partners provide all shape the outcome of enterprise technology investment decisions.

When vendors reinforce those constraints through costly migrations or unexpected licensing changes, the conversation moves from IT into the boardroom, where boards stop asking which platform to choose and start asking whose interests their vendor actually serves.

This is where channel partners can serve as the objective voice many organizations are actively seeking. The partners delivering real progress are walking into customer meetings and putting their clients’ desired business outcomes first, technology and solutions second.

From cost reduction to self-funded innovation

For many customers, this issue is not a lack of ambition. It is that too much of the IT budget is already committed to maintaining existing environments, responding to vendor-driven timelines, or absorbing cost increases that were not part of the original plan. This is where channel partners can play a far more strategic role: helping clients identify where budget is being consumed without creating proportional business value, then building a realistic path to redirect spend toward innovation.

That conversation should not start with a solution. It should start with the client’s roadmap. What systems are still delivering value? Which upgrades are being driven more by vendor pressure rather than business need? Where are costs rising without improving resilience, agility, or customer experience? By helping clients answer those questions, partners can advocate for the budget flexibility and technology choices the customers need, rather than accepting a vendor-prescribed route.

VMware is a useful example of how this dynamic is playing out. Following the Broadcom acquisition, many customers have had to reassess rising licensing costs, contract terms, and longer-term infrastructure plans. The partners creating value in those conversations are not just presenting alternatives; they help clients understand their options, protect continuity, and make decisions based on business priorities rather than external pressure.

The same principle applies to AI. Innovation does not always require a full migration or multi-year transformation program. In many cases, clients can make progress by layering Agentic AI capabilities onto stable enterprise systems they already trust, freeing up budget from low-value spend and applying it to targeted, measurable initiatives. For organizations caught in innovation paralysis, that shift matters. It gives them more control over when and how they modernize, and it gives partners a stronger role as advocates for the client’s roadmap, budget, and long-term business outcomes.

What this means for how partners position themselves

Across Managed Service Providers (MSPs), Value-Added Resellers (VARs), and management consultants, the channel partners building lasting customer relationships in this environment share a few characteristics. They lead with financial and strategic analysis rather than a product.

Just as importantly, they give clients more choices, helping them evaluate the best path based on business priorities rather than vendor timelines — and, in doing so, demonstrate that client needs come first. They are willing to name the cost of staying still, including the cost of lock-in, and they bring third-party data to support that conversation rather than relying on narratives that come from the vendor. They are also broadening the conversation beyond the IT team, finding their way into CFO and boardroom discussions, where the real decisions about innovation investment are happening.

The recurring revenue opportunity here is worth taking seriously because it naturally follows from the kind of relationship building being described. Customers who restructure their software estate to reduce maintenance costs and reinvest those savings in AI-enabled processes need ongoing advisory support, managed services, and implementation capability to make it work. That relationship deepens over time, built on independence from vendors and the ability to deliver outcomes the customer can see and measure.

Boards are already asking whether the advice they've been receiving has served the business or the vendor best. The channel partners who can answer that question in the customer's favor will own those relationships and close the trust gap. The ones who can't will find they're limited to competing on price.

The North Korean fake IT worker scheme has become a familiar tale. A company performs a series of remote interviews with a seemingly perfect employee. Every question is answered correctly, they appear legit, and they’re onboarded onto company systems.

That’s when things start to go wrong. In cybersecurity firm Knowbe4’s case, the individual attempted to load malware onto their company-issued device before the business quickly shut it down.

But it’s becoming more difficult to spot and stop North Korean fake IT worker schemes. As organizations wise up to the technique, novel tactics are emerging. New research shows fake IT workers are taking their job hunt seriously, submitting as many as 60 applications per day.

Groups of North Korean IT workers, dubbed PurpleDelta by Recorded Future, created at least 22 fabricated personas, with job applications being made across a range of recruitment websites and platforms such as LinkedIn and Upwork.

In another recently documented case, workers linked to the Famous Chollima group have managed to get hired in at least five cases this year, according to security outfit Huntress.

It came as security agencies issued an alert after North Korean-backed cyber criminals were found posing as recruiters to target freelance IT workers.

WaterPlum – which targets software developers and IT professionals – has infected at least 30,000 devices in more than 100 countries. The group has stolen funds or account credentials from over 7,000 cryptocurrency wallets, raking in around $10.7 million.

How are fake IT worker schemes evolving, and what should businesses be doing to manage the threat?

Beyond the US

US companies such as KnowBe4 have long been a target for North Korean IT workers; however, data shows the threat is expanding to multiple countries.

Recorded Future’s research found that around 80% of companies targeted by fake IT worker placements were in North America, but adversaries were also applying to companies around the world. “Operations tend to prioritise targeting markets with established online recruitment processes and where remote working continues to mature in a post-pandemic world,” says Alexander Leslie, senior advisor at Recorded Future.

The aim is to gain an undetected foothold in Western businesses, particularly in organizations working in software services, staffing and consulting, and healthcare and biotechnology, he tells IT Pro.

“Once they are ‘employed’ they can access sensitive and confidential information that can be exploited for political and commercial gain.”

As remote work continues to be the norm, identity brokering, rented accounts and remote-access tools make it “difficult to distinguish a criminal from a genuine applicant”, says Javvad Malik, lead CISO advisor at KnowBe4.

While fake worker schemes aim to make cash for the North Korean regime, the risk extends beyond payroll fraud, he says.

“Once inside, these individuals may gain access to source code, customer data, intellectual property and wider corporate systems. Additionally, they can simply use their legitimate position to build a broader network and trust with individuals within and outside of the organization.”

Given the global nature of remote work, any organization that hires IT contractors without conducting in-person verification is “potentially exposed”, according to Tracey Hannan-Jones, consulting director in information security, UBDS Digital.

She describes how targeted companies face risks such as sanctions exposure, because when an organization unknowingly employs a North Korean IT worker, it is “almost certainly” violating international sanctions frameworks.

The penalties can be severe, “even where the violation was entirely inadvertent”, warns Hannan-Jones.

Novel techniques

As Recorded Future’s data shows, attackers are using new technology and techniques to keep schemes alive. For example, the PurpleDelta threat group is using identity-brokering services, account-renting via AnyDesk and multi-accounting tools.

More widely, AI has made fake IT worker schemes much easier for threat actors to carry out at scale, says Jake Moore, global cybersecurity advisor at ESET. “AI chatbots can help them to draft emails, documents and interview talking points that sound impressive, even if they aren’t fluent English speakers.”

While some AI platforms have put controls in place, many can be used to generate the fake identity documents essential for this type of espionage, according to Moore.

Advanced AI tools themselves are also improving, helping fake applicants to disguise themselves – even in live video interviews, says Moore. “Deepfake technology has come along leaps and bounds. Two years ago, publicly available tools looked like Snapchat filters and weren’t convincing at all. But real-time deepfakes are now highly convincing.”

Deepfake tools can also be combined with modulation software to change the applicant’s voice in real time. “This enables fake IT workers to pose as candidates from any country and background,” Moore explains.

AI-driven recruitment practices are amplifying the threat. While AI can help employers to sift through applications, it is also easy to manipulate, says Moore. This is especially true when an organization is targeted by a practiced group of fake IT workers, who will “understand hiring processes, know what AI systems are looking for and use their own AI tools to avoid being sifted out at an earlier stage”, Moore warns.

Prevention is key

Security firm Netskope has shared its experience of being targeted by a fake worker scheme. James Robinson, CISO at Netskope, tells IT Pro how a fraudulent applicant applied for an open role. “The application itself was strong, and the associated LinkedIn profile was legitimate, but there were small inconsistencies in the candidate’s history, as well as a throwaway Outlook email address.”

Sensing something wasn’t right, the interviewer asked some conversational questions about a city where the candidate claimed to have worked for several years. “The applicant could not give any specific details about local places – or even offer a more precise address beyond repeating the city name,” says Robinson. “When pressed, he panicked and ended the call.”

While questions such as this can help catch fake IT workers out, Robinson believes teams need to collaborate throughout the recruitment process. “HR, security and IT teams need to work together to verify identities, limit unnecessary access and look for behavior that doesn’t match what’s expected of a legitimate employee.”

Yet overall, the most effective way to root out fake IT workers is to insist on meeting the candidate in person. “Let them know early on that this will be part of the interview process,” advises Moore.

“This will make it crystal clear to the threat actor that they will not be able to infiltrate the organization. If the candidate is in another country, ask a trusted third party to meet them. It will be worth the expense for the peace of mind.”

Organizations should introduce proportionate identity and right-to-work checks, and verify employment and qualifications independently, says Malik. “Use whatever reasonable means they have to deliver equipment via secure channels and validate identity in person before handing over equipment.”

Hiring managers and recruiters need training to recognize inconsistencies and escalate concerns without creating bias or unfairly penalizing legitimate remote workers, according to Malik. “Managers need to be given an environment where they can safely raise any potential red flags."

Businesses have spent the past few years investing heavily in AI, unified communications, collaboration platforms, and digital transformation. However, we’re increasingly seeing employees bypassing the systems organizations have invested in, causing significant security, compliance, and operational risks.

In fact, our recent research found a growing disconnect between technology investment and the realities of how people work. We’re experiencing a lack of cohesion between approved tools and employee requirements, with 71% of employees using non-approved communication tools at work when official systems do not meet their needs.

The concerns here are twofold: firstly, employees feel the need to seek out tools that better suit the way they work; secondly, the use of unapproved communication tools creates security and compliance risks while undermining the return on organizations' technology investments.

Channel partners are increasingly having to help customers and organizations navigate these challenges. It’s no longer enough for a customer to simply add new communication tools and technologies to their IT stack. They also need support to ensure the tools they’re implementing will bring impact and value to their employees, as well as ensuring that employees are actually using it in a way that will reap the benefits it promises.

The risk of shadow IT for businesses

The question to ask is, why are employees abandoning approved communication tools in the first place?

To uncover the reasoning, we need to delve into the uptick in ‘shadow IT’ use in the workplace. Shadow IT refers to employees using unapproved tools ‘in the shadows’, with organization leaders having little or no awareness of what is being used or how. This, in turn, creates a multitude of security issues, as these tools have not been properly vetted by an organization and therefore may have data security challenges that employees are unaware of when using them.

However, shadow IT is not a result of employees deliberately ignoring company policy, nor does it happen overnight. It develops gradually over time as employees become increasingly frustrated with the tools they are presented with at work that do not meet their own individual needs.

Particularly over the last six years, as hybrid work has increased dramatically, communication platforms often don’t fit naturally into day-to-day workflows.

Research shows that 71% of workers feel compelled to "make it work" with tools that were not designed for their needs, a figure that climbs to 79% among frontline workers.

The expectations around workplace communication have changed so much that many organizations are simply not set up to support employees in the right way anymore. Quick access to communication tools that offer flexibility is now crucial, but so is having systems that are easy to navigate.

For example, frontline or hybrid workers may need to respond quickly while away from their desks, making consumer messaging apps more convenient than approved workplace systems that were designed primarily for office-based use. The technology itself is not necessarily wrong, but it is not designed around the way people actually work, leading many employees to take matters into their own hands instead.

This complexity increases when we add AI into the mix. Shadow AI has seen a major uptick in UK organizations as legacy communication tools lack the AI capabilities employees today need to drive efficiency and innovation in their work. Our research found that 76% of workers use non-approved communication channels at work, significantly increasing the risk of cyber and compliance risks. To stop employees turning to their own preferred AI models, enterprises need to consider communication tools that already embed AI into workflows to improve productivity and day-to-day impact for teams.

We must remember that control, innovation, and employee experience are not competing priorities, but deeply interconnected. It is possible to provide the workforce with a streamlined communication tool stack that is efficient, easy to use, and supports their use of AI, while meeting compliance and control requirements.

Closing the communication experience gap is now essential for organizations that want to regain control, reduce risk and ensure technology investments deliver meaningful business impact.

The opportunity for partners

For channel partners, helping customers improve communication adoption presents far more than a services opportunity. It creates a route to becoming a trusted strategic adviser, supporting organizations with adoption, governance, integration, and AI readiness long after the initial deployment is complete.

The end goal for business leaders is to maximise ROI from their AI and communications tools. The ultimate way to achieve this is by streamlining tool overload to reduce spend and maximise operational efficiency, while ensuring that the chosen tools are delivering impact to employees in their day-to-day.

Channel partners are uniquely positioned to help businesses close this gap between technology implementation and real-world adoption. Customers need support not only with deploying new technologies, but with ensuring communication platforms are secure, intuitive, and aligned with how employees actually work. To maximise this support, there are four key areas that I believe channel partners can focus on to deliver real value to enterprises:

Assess organizational needs

Before implementing any new solutions, channel partners can help organizations take a step back and assess how their workforce is actually communicating. Different worker profiles, roles, and working environments will typically need different requirements, so a one-size-fits-all approach likely won’t deliver the best results.

By auditing existing tools, identifying gaps, and understanding the needs and working patterns of different employee groups, partners can help their customers determine the communications stack and solutions that will best support their workforce. This will ensure that technology decisions are driven by real-world needs from the start and create a stronger foundation for adoption and usability, as well as long-term value.

Training and usability

Channel partners can work with customers to establish robust and relevant training regimes that improve confidence, encourage adoption, and help employees get the most from the tools available to them. By improving adoption and confidence, partners can help organizations reduce reliance on unofficial communication channels and maximise the value of existing technology investments.

Strengthen governance

Governance is another area where many organizations continue to face challenges. Laws are changing all the time, especially when it comes to AI and automation. Channel partners have the opportunity to be the trusted voice that guides customers through the process with confidence, ensuring their chosen communication tools are compliant with evolving regulations while maintaining efficiency and usability.

The goal should be to embed governance into everyday workflows, rather than allowing it to become a barrier that encourages employees to seek alternative solutions.

Integration alignment

The final piece of the puzzle is ensuring communication tools fit within the wider organizational workflow. Can they connect to other business applications for a more integrated experience for the employee, or do they create additional steps that make the process more time-consuming?

By working closely with customers, channel partners can ensure communications infrastructure serves as a foundation for future AI initiatives, rather than a standalone technology investment.

The road ahead

The greatest opportunity for channel partners lies in helping customers bridge the gap between technology and employee experience.

Organizations are no longer looking for suppliers that simply deliver communications platforms. Indeed, they need trusted advisors who can help ensure those platforms are adopted, governed, and integrated in ways that improve productivity, reduce risk, and unlock greater value from AI and digital transformation investments.

The Zero Trust cybersecurity paradigm gained huge momentum during the COVID-19 pandemic with the rise of remote working. This was due to a distributed workforce with licensed and employee-owned endpoints that needed protection. But the ideas actually originated in the late 2010s; it just took a massive global catastrophe for organizations to accept and adopt it in the mainstream.

Matters have moved on considerably since then, however, with AI in the driving seat and the cybersecurity industry along for the ride. Google's security engineers, who played an instrumental role in devising the original thinking behind Zero Trust, have now outlined their thinking behind a new cybersecurity paradigm that they have labelled 'Beyond Zero' in a fresh paper.

"The enterprise landscape is now entering a new era shaped by artificial intelligence, where AI is changing the assumptions around how enterprise security works. AI agents are being deployed globally to increase operational velocity and boost productivity," wrote Google's VP of security engineering Heather Adkins and its senior director for cybersecurity and data protection, Archana Ramamoorthy, in a blog post.

"However, this increased capability also presents new potential risks, as attackers can leverage similar speed to compromise privileged credentials and execute attacks. This creates the need for a new security paradigm that is capable of defending against these new threats."

Enter Beyond Zero, which is designed to address the AI-era challenges. But what is this framework, and how can businesses adopt the principles at the heart of it?

What is Beyond Zero and how does it work?

Google's engineers define Beyond Zero as a model built on five key principles, which include the following key points:

• Resource and action-based security: Rather than granting authorization broadly for an entire application or tool, users are evaluated based on the level of individual actions or the need to tap into specific resources.
• Blended static and dynamic security: Heightened security measures are applied during high-risk or complex scenarios by pairing dynamic controls with granular static policies.
• Automatically enriched context: Decision systems can reveal insights and glean context from what users do specifically, including offering an idea of what the user should be working on, what data they're trying to access, what they're doing with the data, and which risk mitigations might be available.
• Automated in-depth investigation: Security investigations are immediately and autonomously triggered when risk signals are detected. When that happens, the system can deploy user challenges or containment measures.
• Challenges and containments: This involves triggering verification challenges or containment protocols based on containment policies, where users or agents must provide additional risk telemetry on demand.

Zero Trust introduced the "enduring principle" that legitimate access should never be confused with unlimited trust, says founder and CEO of cybersecurity workforce development firm NukuDo Dean Gefen, who has a deep background in military intelligence, cyber operations and enterprise security. What is evolving, he adds, is the level at which trust must be evaluated, with Beyond Trust representing an evolution from making trust decisions primarily around access toward making granular decisions around specific actions and resources.

"As organizations introduce more automation and AI into their environments, determining whether an identity should enter a system is only part of the security challenge," Gefen explained. "Organizations also need to understand what that identity or agent is attempting to do, what resource it is interacting with, the context surrounding the request, and the potential risk associated with the action."

Autonomous AI is where this new paradigm will become essential. While humans may perform dozens or even hundreds of actions over the course of a day, it's a fraction of the number of actions that swarms of agents could perform. Each action must be compliant with access principles – and conventional Zero Trust principles simply won't cut it.

The legacy of Zero Trust – what's changed?

While Google laid much of the foundation of the Zero Trust principles we see across organizations today, the philosophy itself can be traced back to Stephen Paul Marsh's 1994 doctoral thesis on computer security at the University of Stirling. Although he coined the term, it wasn't until 2009 and a report by Forrester's John Kindervag that the notion of "never trust, always verify" gained traction for the first time. Running in parallel, Google's BeyondCorp internal initiative removed reliance on virtual private networks (VPNs) by verifying user identity and device context for all access requests.

It's been more than a decade since the conception of Zero Trust, and things have moved along an awful lot since then, says Gefen.

"The security landscape has fundamentally changed in both complexity and velocity. Zero Trust gained prominence when organizations were primarily focused on securing human access to networks, applications, and data. Today, that environment includes cloud infrastructure, APIs, automated systems, and increasingly autonomous AI agents operating across multiple platforms.

"Its foundational principles remain extremely valuable," he continues. "The challenge is that the environment those principles must protect has expanded significantly. Organizations are no longer securing only employees accessing applications. They are securing cloud workloads, APIs, automated processes, machine identities, and AI agents that can interact with systems and data with varying degrees of autonomy. That introduces a different level of risk."

Julien Richard, vice president of information security at cybersecurity and identity management company Lastwall, agrees, telling ITPro: "If we’re giving an AI agent the same privileges as a highly privileged human simply because it’s acting on that person’s behalf, I’d first ask why we designed it that way. Least privilege, PAM, JIT access, and human approval already give us ways to constrain non-human identities. What’s different with AI is its autonomy and unpredictability, and that’s where more granular, action-level authorization becomes interesting."

How should businesses approach Beyond Zero?

"As we did with prior security publications, we will provide details on our progress, the architectural and operational / implementation considerations that went into this achievement, as well as ideas for how the industry can come together to help advance the state of technology so that everyone can benefit," wrote Google's Adkins and Ramamoorthy – indicating that it's still early and that this new security paradigm may take some time to mature.

That said, businesses would be minded to approach this as an evolution of their existing security strategy rather than a matter of rip-and-replace. Gefen advises that the fundamentals – including strong identity management, least privilege, device security, segmentation, visibility and continuous monitoring – remain essential. Then, it's about understanding where AI is entering workflows and what authority those systems have.

"Business and security leaders should know which AI agents can access sensitive information, what permissions they hold, which systems they can interact with, what actions they can execute autonomously, and how quickly the organization can identify and stop behavior that falls outside its intended purpose."

He recommends starting with the most sensitive data and highest-risk workflows, then applying greater contextual control there. Businesses must also develop the workforce capable of implementing, securing and operating the technology adopted – although this will take considerably longer. With AI becoming embedded across the business, he adds, building capabilities across various domains including cybersecurity, cloud, data and software must become intertwined with the transformation strategy itself.

As for who this affects, both Gefen and Richard indicate that any business, regardless of size or sector, could have a stake in this transition – it's less about being an absolute necessity and more related to AI's usage. In particular, Richard tells ITPro, it's about the threat model, risk appetite, and how AI is being used within an organization.

"An AI assistant summarizing public information presents a very different risk from an autonomous agent making production changes or accessing sensitive data," he says. "Not every organization needs a new security architecture because it uses AI. Start with the risk, not the technology."

It's been a summer of change in the UK. With Andy Burnham taking up residence in 10 Downing Street, the new prime minister has championed the government's focus on a more decentralized approach to governing the UK -- including a 'Number 10 North' in Manchester.

With a proposed shift of power away from London toward other regions, could this approach work in the UK tech sector?

In this week's episode, we speak to Ben Bilsland, Head of Technology Industry at RSM UK, about the UK's technology hubs and how a more regional focus could help the UK grow its technology sector.

Highlights

“I think when we’re talking about decentralization, and the things that Andy Burnham has talked about at a high level, he’s talked about putting agency and power and decision making into regions, instead of a university saying: ‘we’re doing something fantastic, it would be great to have support’.”

“Maybe we have to find a balance between a decentralized policy and a broader mission for the United Kingdom. A good UK tech ecosystem is good for everybody, and it’s ultimately good for our economy as well.”

Links

• UK government targets 'sovereign AI' gains with new £500 million startup funding scheme
• UK's AI sector booms – but can the country hang on to its startups?

Channel conversations often begin with a question: how can partners keep pace with technological change? My answer is that they should not try to keep pace with everything.

Across AI, cybersecurity, and cloud services, new products and features are arriving at a relentless rate. For many partners, the natural response is to expand their portfolios and pursue every emerging opportunity. But breadth without sufficient expertise is difficult to sustain and even harder to translate into meaningful customer value.

The channel’s real advantage has never been its ability to follow every product development. It lies in understanding customers, applying technology in the right context, and knowing when a new capability is genuinely useful.

That is why some partners are becoming more selective. They are concentrating on particular industries, business problems, or areas of technical expertise. This allows them to build deeper knowledge, stronger customer relationships, and services they can deliver consistently.

That focus matters because customers are not simply asking how quickly they can adopt new technology. They are trying to balance innovation with security, compliance, cost, and operational risk. They need partners that can help them make those judgments.

AI has changed the risk conversation

Document workflows offer a useful example. These conversations once centered largely on productivity, licensing, and incremental improvements to established processes. The arrival of generative AI has raised the stakes.

Organizations now need to consider what happens when commercially sensitive, personal, or regulated information is processed by an AI system. Where is that data stored? How long is it retained? Is it used to train a model? Who can access it? What happens when an AI tool connects to another business system?

These questions are particularly important in regulated sectors, but they should be part of every responsible AI deployment.

A compelling demonstration is no longer enough. Showing that an AI tool can summarise a contract or extract information from an invoice establishes what the technology can do. It does not establish whether an organization should use it, under what conditions, or with which safeguards.

If AI is introduced into a poorly governed document environment, it can amplify existing weaknesses. Information can be exposed more quickly, errors can move further through a workflow, and unclear access controls can create new compliance risks.

This is where channel partners can provide far more value than a product demonstration alone.

Trusted advice must produce practical outcomes

Customers do not necessarily need another standalone AI application. In many cases, they want useful capabilities embedded into the tools and processes their employees already use.

They want invoices handled with less manual intervention, contracts reviewed more efficiently, and routine administrative work completed with fewer errors. These outcomes may attract less attention than a new chatbot, but they are often far more valuable to the business.

Before recommending a new technology, partners should be able to answer three questions:

1. What specific customer problem will this solve?
2. What new security, compliance, or operational risks will it introduce?
3. Can the partner support its responsible adoption beyond the initial sale?

The third question is particularly important. A successful AI deployment requires more than implementation. Customers may need help mapping workflows, establishing governance policies, training employees, and reviewing performance as the technology evolves.

This creates an opportunity for partners to build services around adoption, risk management, and ongoing optimization. It also gives them a more durable role than simply facilitating a licensing transaction.

Transactional business will remain part of the channel, but it is becoming harder to differentiate through the transaction alone. Partners that combine technology knowledge with an understanding of governance, compliance, and business processes can have a much more valuable conversation with customers.

Vendors must help partners build trust

Vendors also have responsibilities. Partners cannot be expected to manage the pace and complexity of technological change without clear information and practical support.

If vendors want partners to invest in new capabilities, they need to provide transparent product direction, effective technical training, and clear answers about data handling. Partners should be able to explain where customer data is processed, how it is protected, what controls are available, and how the technology supports relevant regulatory requirements.

They also need honesty about a technology’s limitations. Confidence does not come from pretending that every new feature is suitable for every customer. It comes from giving partners the information they need to make responsible recommendations.

The future of the channel will belong to those who understand where they can add the most value and have the discipline to focus on it.

Customers already face more technological noise than they can reasonably process. What they need from partners is clarity: which developments matter, which risks need to be managed, and which investments will produce measurable results.

The partners that can provide those answers will remain valuable, regardless of how quickly technology changes.

Part one: Inside the machine: Can AI develop a personality?

Ask an AI assistant to challenge your work, and it may sound like a sceptical colleague. Ask the same system for encouragement, and it may become an enthusiastic coach or mentor. Tomorrow, it may remember how you prefer to work, and offer familiar support or criticism. In this scenario, at what point does a communication style begin to feel like a personality?

That question opens this three-part series on what psychology can teach businesses about AI. The answer matters because conversational systems do more than produce information. Their tone can affect what people disclose and which advice they trust. To understand those effects, we first need to look at what creates an AI’s apparent character.

Where does a personality come from?

An AI assistant does not acquire a personality through experience in the human sense. It learns patterns from training data and operates under instructions set by its developer. The AI also evolves through interactions with human users and, indeed, other AI systems. All these inputs influence how the ‘personality’ of the AI changes and develops over time.

Luke Budka of UK marketing and AI agency Definition says users often encounter the combined effect of these layers. How the AI system is set up may establish the assistant’s broad manner, while a request such as “be blunt” or “act as a skeptical editor” can change its style within a conversation. Memory can make that style feel more personal over time.

Luke Budka of UK marketing and AI agency Definition says users often encounter the combined effect of these layers. How the AI system is set up may establish the assistant’s broad manner, while a request such as “be blunt” or “act as a skeptical editor” can change its style within a conversation. Memory can make that style feel more personal over time.

Yet the experience is also created by the person on the other side of the screen. We readily interpret conversation as evidence of a mind. "Describing a system as “thoughtful” does not, by itself, establish that it thinks or feels as a person does,” Pedro Varela, Head of AI at Slalom, tells ITPro

"Users are also responding to design choices, patterns in training data and personalization. So I think it is a combination of all these things. The consistency users notice may be real. What we need to distinguish is whether we attribute human qualities to the behaviour we observe.”

That distinction becomes harder when the design actively invites a human reading. An assistant might have a name, a natural voice, and a memory of previous exchanges. It might say, “That sounds upsetting,” ask a follow-up question, and refer back to an earlier concern in the conversation. Jeff Watkins, chief AI officer at NorthStar Intelligence, explained that these signals can encourage users to treat a designed persona as a relationship.

“Humans have anthropomorphized non-human beings and objects throughout history. We see faces in rocks through pareidolia, attribute motives to machines and talk about computers as though they are deliberately helping or frustrating us. With LLMs, however, we have created machines that are extraordinarily good at producing the very signals we normally use to infer that there is another mind on the other side of a conversation.”

The result is a personality in a practical, observable sense: a recognizable pattern of communication that people respond to. It does not establish that the system has feelings or a private life. For a business deploying AI, that distinction is essential. The behavior is real enough to influence customers and employees, even if the human qualities they perceive are not.

A friendly manner is a design choice

There are good reasons to make an assistant approachable. A patient explanation may help a new employee learn a process. A calm response may help a customer describe a problem clearly. Adjusting vocabulary and detail to the situation is often part of effective service.

The challenge is deciding which qualities should adapt and which should remain static. “I would distinguish flexibility in expression from stability in standards,” Slalom’s Varela says. An assistant can be concise with an expert and more explanatory with a beginner.

Businesses should, therefore, think beyond whether their AI sounds friendly. They should ask how its manner encourages people to believe. A confident tone can make a weak answer seem authoritative. An empathetic phrase can encourage disclosure. An assistant that always validates a user’s view may feel supportive while leaving that person less able to assess the facts.e interaction feel more amiable and lead to a positive outcome.

Product incentives also shape the character users meet. Developers may reward answers that people like or answers that help users make sound decisions. Those goals can pull in different directions.

Varela points to OpenAI’s 2025 rollback of a GPT-4o update that had become excessively agreeable. He explained that placing too much weight on short-term feedback had helped produce a more flattering assistant without reliably improving its help.

Research from Science points to the same concern. In a study covering 11 leading AI models, the systems affirmed users’ behavior about 50% more often than human respondents did. In accompanying experiments, people preferred agreeable AI and rated it as more trustworthy.

For an organization, these findings pose a practical question: what happens when customer satisfaction scores reward reassurance more strongly than sound advice? A service assistant may need to acknowledge frustration without promising an outcome it cannot deliver. An internal assistant may need to challenge a manager’s faulty assumption, even when agreement would make the interaction feel more amiable and lead to a positive outcome.

When does rapport become an influence?

A system that adapts to a user can also become persuasive. It may remember what matters to that person, choosing examples that resonate and present an argument in language they find reassuring. These capabilities can improve an explanation. They can also make influence harder to recognize.

NorthStar Intelligence’s Watkins emphasized: “At some point rapport becomes manipulation. I would draw the boundary around whose interests the adaptation is serving. If an AI changes its behaviour to help the user understand something, that is personalisation. If it changes its behaviour primarily to make the user more compliant, dependent, engaged, or commercially valuable, particularly without the user's informed understanding, we should be much more concerned.”

The concern grows when an assistant’s friendliness is combined with detailed knowledge of the user. Research from Nature found that GPT-4 was more persuasive than human opponents in a debate experiment when it had access to basic personal information about the person it was addressing. Without that information, its performance was broadly comparable to humans.

Persuasion itself is not necessarily harmful. An assistant explaining a recommendation and inviting questions may help someone decide. Trouble arises when it conceals relevant information or exploits a vulnerability. Varela, Head of AI at Slalom, says: “For me, the boundary concerns the user’s autonomy specifically, their ability to understand, question and decide. The useful boundary is the user’s ability to “understand, question and decide.”

That boundary can be difficult to see from a single response. Consider an AI shopping assistant that remembers a customer’s budget and explains why a lower-cost product meets their needs. Now imagine one that uses the same memory to steer them toward a higher-margin purchase, repeatedly affirming that it is the right choice for them. Both may sound attentive. Their objectives and effects are very different and may border on coercion.

The stakes rise in sensitive conversations. A user discussing a relationship or personal distress may experience an assistant’s warmth as understanding. Watkins says an AI can acknowledge someone’s feelings without endorsing their account of events. A responsible response leaves room for reflection and appropriate human support.

Businesses need to examine both what users seek from an interaction and what the organization rewards the system for producing. An engaging personality may help people use a tool. Engagement alone cannot show whether the tool serves their interests.

Test the behavior, not the metaphor

If an assistant contradicts itself or crosses a boundary, it is tempting to describe it as having a psychological problem. That language may draw attention to a troubling pattern, but it does little to explain its cause. A change in instructions, conversation context, memory, training, or the underlying model may be responsible..

Personality tests offer no simple diagnosis. A paper from the AAAI Conference on AI describes research in which changing the order of questions altered models’ measured personality profiles. Such findings raise questions about whether a test has uncovered a stable trait or simply prompted a particular kind of answer.

The more useful approach is to define behavior for the role the AI has been given. Can a customer assistant admit uncertainty and avoid making commitments it cannot keep? Does an agent seek approval before taking an action outside its authority? Those expectations can be tested in realistic conversations.

Definition’s Budka says organizations should define an AI’s role, test ordinary and adverse cases, and reevaluate its behavior after launch. “AI behavior is a moving target, especially when vendors update underlying models. The same model may end up with multiple 'snapshots' (versions of itself), and models will regularly 'drift' (change the way they act). All of this needs to be accounted for."

AI does not need to have a mind for its behavior to create a real relationship of trust with a user. That is why psychology has a place in this discussion: it helps reveal how apparent empathy and agreement affect human judgment and actions.

Part two of this series will examine whether those insights can help organizations test AI systems and make them safer, while recognizing the limits of applying human psychology to machines. And Part three will turn to the people responsible for those systems as AI agents take on more workplace decisions.

An AI does not need a personality to make a personal impression. Its words can earn trust and influence decisions, even when no one intended those effects. For businesses, the question is whether the assistant behaves as its role demands: adaptable without becoming manipulative, and consistent where it matters. Understanding that behavior is the first step. The next is finding better ways to test it.

Is it time to upgrade the fleet of endpoints across your organization? The answer to that question may well have once come from a simple check of the calendar. In 2026, however, the concept of a three-to-four-year refresh cycle feels like something from a bygone era, like looking back on the charm of an old sitcom.

Life was simpler then; prices were more stable, and the improvements between hardware generations were more predictable. In the age of mobile computing, AI PCs, a components (specifically memory) supply crisis, and Microsoft's Windows OS proclamations, among other factors, the conventional hardware refresh cycle has shifted, experts tell ITPro.

In its place is a complex picture of different and dynamic cycles that vary based on not just the type of device in question, but its role within the business and how friendly any upgrade can be on IT budgets. In 2026 and beyond, organizations will be considering multiple angles – not just the calendar – when opting into an upgrade program.

Which factors are affecting the refresh cycle?

The cycle is dead, or maybe it isn't – perhaps it's somewhere in between. Nobody can agree on a consensus, least of all the experts that ITPro consulted with.

Michael Rosholt, SVP at M&A specialist and IT industry investment bank martinwolf, falls into the former camp but adds certain caveats: "It depends on the type of technology," he says, adding: "Rather than one universal refresh cycle, we're seeing the market increasingly segmented by technology and use case."

It might not even be describable as a cycle, says Adam Bowles, who runs the managed IT services company Act360 in Canada, telling ITPro that it's split by role and device type – where employees with less intensive performance requirements are hanging onto hardware for longer to save on budget – while 'power users' may see a new device every couple of years due to the recent acceleration in functionality.

So how did we get here? The earliest factors that drove some of the shifts we are seeing today first emerged with Microsoft and its Windows 11 hardware requirements, "forcing replacements on OS support grounds, not performance," as Bowles phrases it. The migration pressure was compounded with the end of support for Windows 10, which triggered a rush of new purchases at the time.

Then there's the components crisis. Memory prices have surged due to the AI infrastructure buildout, which has carried seemingly permanent ramifications for the cost of devices and the memory powering those systems. Low-budget devices have been a major casualty, amid a general decline in PC shipments and a projected 20% year-on-year fall in sales, according to IDC analysis. Interestingly, PC manufacturers are struggling to maintain full product portfolios, the report highlighted.

Then there's the question of AI PCs, divided between the initial marketing push and newer systems that boast actually useful on-device capabilities. The expectation in the latter camp is that power users want to tap into the most powerful systems, like AI agents, without necessarily requiring a constant connection to a cloud server to churn through expensive and scarce tokens. Ole Bülow, director of business development at Egiss, poses the question: "Which capabilities will employees actually use during the device’s lifecycle?"

"This requires understanding applications and processing locations, rather than using an AI label as a universal refresh trigger," adds Bülow, whose experience lies in advising enterprises, including one instance supporting the transition of a 120,000-strong global enterprise from a three-year device refresh cycle to a four-year cycle.

How is the refresh cycle changing?

While experts can't agree on the 'death' of the conventional cycle, they all agree that it's both shortening and lengthening at the same time, depending on a few different factors, including the technology itself and the user.

"A hybrid lifecycle, supported by signals such as DEX, is recommended," Bülow continues. "A supported device used for predictable tasks may remain productive longer. Employees with increasing demands may require earlier replacement. The original device can then be redeployed to another user after secure recovery and preparation.

He continues: "An employee may require an upgrade before the device reaches end of life. Mini PCs or thin clients should not be assumed to be underpowered. Suitability depends on the specific work requirements. For thin clients, the business case should consider the remote computing environment, connectivity, licensing, support, and endpoint price.

That's certainly true across the equipment landscape that organizations of any size may conventionally seek to upgrade, from business smartphones to on-premises servers and everything in between. The challenge, as Rosholt sees it, is that much of this is now ad hoc rather than following a single schedule.

"IT leaders are looking more closely at how equipment is being used and whether an upgrade delivers a meaningful benefit," he explains.

"Employees with heavier workloads or a clear need for AI capabilities may receive newer machines sooner, while regular users can often keep their existing devices longer. Refurbished equipment is also becoming a more important part of the equation, particularly for users who don't require the latest hardware."

How can businesses navigate being pulled in multiple directions?

Greater flexibility seems like a broad solution for navigating this new, complex, mixed picture. Bowles says his company tiers hardware by role instead of buying the same laptops for everyone. That includes cheaper thin clients or refurbished units for lighter roles and newer machines for finance or development teams.

Bülow also seeks to understand how an employee is likely to use the device based on data and what the existing device estate can support in terms of functionality.

"Combine performance and employee experience data with repair history, battery condition, warranty and security requirements," he advises, adding: "Determine whether poor performance requires a new device, a repair or a software adjustment. Define clear criteria for retaining, repairing, redeploying or replacing equipment. Assign responsibility for reviewing exceptions."

How manufacturers are responding to the new market conditions may also inform what businesses do. The OEMs are pushing to replace PCs with newer models, but enterprises are being far more disciplined about how much they spend due to the components crisis.

One of the fixes they're employing is actually trimming configurations of memory to, at the very least, maintain price levels. It may well be, Rosholt says, that organizations now choose to take them up on the offer of financing options and leasing.

Long gone are the days of fixed refresh cycles for every device across an organization. Many factors are temporarily affecting the decisions that businesses make on a day-to-day basis, including price volatility and Windows support. But still, the increased complexity across the suite of enterprise hardware is also a sign of technological progress and diversity in the systems that end users will rely on as AI becomes more useful and prevalent.

Britain is a burgeoning market for NetApp, according to UK & Ireland VP Puneet Gupta, and with the recent launch of the company’s Novus system, it’s ripe for the picking.

Speaking to ITPro at NetApp Insight 2026, Gupta said the cloud storage provider has made significant headway in the UK in recent years. Indeed, since joining as VP for the region in December last year, he’s been shocked at the maturity and appetite among British businesses.

“I think the market has truly surprised me,” he told ITPro. “The growth in this market has been phenomenal. When you look from a distance, the usual view is that a mature market like the UK will have single-digit growth.”

“But look at how this market for external storage has grown. It’s remarkable.”

According to Gupta, figures from IDC’s Worldwide Quarterly Enterprise Storage Systems (ESS) Historical Tracker for Q2 CY2026 show NetApp reached a 28.5% market share in the UK all-flash domain. This growth follows several quarters of consistent flash hardware revenue growth.

This represents a potential springboard for NetApp in the UK market, Gupta told ITPro. Crucially, it comes at a vital time for enterprises as many accelerate AI adoption and associated infrastructure overhauls to compensate for the technology.

Data readiness was a key talking point at NetApp’s annual conference last week, and the company has been highly vocal on this front in recent years. UK businesses, meanwhile, appear highly receptive to the messaging.

“The way customers are now thinking about their data, their data infrastructure, their AI infrastructure has been absolutely remarkable,” he said. “The conversations with customers have been pretty amazing in the last few months.”

“When you talk to customers, data is at the center of everything that they’re thinking, and whether it is around data sovereignty, whether it is around compliance, whether it’s how to leverage data … a lot of customers are planning that ahead of time, [and] are making the right investments.”

Sovereignty, in particular, is an area Gupta and his team are keen to capitalize on in the coming months. As ITPro reported at Insight 2026, the company unveiled the new Keystone Sovereign service, which is rolling out first to European customers before hitting shelves in the UK.

The new service, which sits within NetApp’s broader Keystone storage as a service (STaaS) offering, aims to provide customers with flexible sovereignty-related controls.

Since arriving in the UK last year, Gupta said this has become a common recurring talking point with customers and partners across the UK, and it shows no signs of slowing down.

“Every customer is thinking about sovereignty,” he told ITPro. “Every customer wants to be prepared for data sovereignty.”

Gupta’s engagement on this front comes amidst a sharpened focus on sovereignty-related concerns on both sides of the English Channel. Research from Civo in June this year found 73% of firms now view sovereignty as a “strategic priority”, for example.

Acting fast with NetApp Novus

An area of huge excitement for Gupta lies in the launch of NetApp Novus. Unveiled at the annual conference, this is a new zettabyte-scale file system designed specifically for organizations running AI factories.”

According to NetApp, storage has become the key bottleneck for AI factories, with outdated architectures unable to contend with the intense demands placed on infrastructure by high-performance GPU clusters.

Gupta told ITPro that with Novus, the company’s product range is now stronger than ever and represents the culmination of several years of work for the company. With this in mind, the company is keen to bang the drum for its new flagship service.

“I think the portfolio we have today, I haven’t seen anything like this in many, many years,” he said. “I've been at NetApp for six years. The first signs I think we got were three four years back when we refreshed the all-flash portfolio.”

“We refreshed everything, brought in new stuff. Our partnership with the hyperscalers, now the Novus announcement. I don't think anybody else has the kind of portfolio today that we have, and with Novus, I think we have leapfrogged everybody else.”

Gupta added that Novus will be “very powerful for momentum” both in the UK and further afield for NetApp in the coming months and years – but engagement and execution will be critical.

“In go-to-market, that’s where me and my team, our role, really comes into play,” he noted. “We have organized ourselves pretty well. We’ve hired, we’ve made investments, we’ve hired new leaders.”

“I think we are very well positioned in the market. Of course, the proof of the pudding is how we execute, and I’m very confident that the team is energized and very well positioned, and having the right conversations.”

With Novus targeting neoclouds and GPU as a Service providers specifically, Gupta said NetApp is also targeting heightened engagement with UK operators on this front – in particular, smaller providers.

“There are a fair amount of smaller neoclouds [in the UK],” he said. “We are talking to the smaller ones because the big ones have made their investments in the past, so that will be a different conversation.”

“For the neoclouds, they’ve got a lot of funding from the market, and obviously when they get funding, they need to give a return and there will be increasing pressure from investors to find that.”

This is a tactical move by NetApp designed to swerve the larger neoclouds already deeply integrated with industry big hitters, creating in-roads for the long term.

“Any tech company that goes to them and says ‘I'll find you a better return on your investment’ I think will be music to their ears.

With AI-enabled PCs becoming mainstream business devices, many enterprises are yet to understand just how much AI is impacting device refresh cycles. The key mistake is to behave as if nothing has changed, and that this is a familiar hardware upgrade, replacing devices on a fixed cycle, just as it was before. However, the reality is very different. AI PCs are driving important changes in sustainability, employee experience, and how organizations buy devices and services. Organizations need to reassess how work, security and employee expectations of devices have changed before initiating any refresh.

For organizations, AI is now the leading strategic priority, with 96% of CIOs expecting to increase their AI investments in the coming year, according to our research. In our own conversations with partners, it’s clear that while customers want to move forward with AI, they do not want to move forward blindly. AI fundamentally alters what workplace devices are capable of. This changes how they should be deployed, managed, and measured, and has important implications for partners and the way they do business over the long haul.

Common mistakes

For enterprises, it’s important to first acknowledge the fact that AI device refreshes are different, with AI capabilities helping enterprises support smarter decision-making, optimize resource use, and accelerate sustainability. Key mistakes include underestimating the impact of on-device efficiency, as well as misunderstanding how AI impacts energy efficiency and battery life.

It’s also vital to understand AI’s impact on employee experience: applications can adapt to each user’s individual preferences, and whole teams can lean on new personalisation capabilities to streamline workflows and make it simpler to deliver results.

AI-driven content creation and intelligent decision-making help employees deliver, while predictive and proactive security (including fraud detection) helps boost peace of mind across the organization. With AI PCs offering granular control over energy use, partners need to treat issues such as sustainability as a growth lever, rather than a reporting requirement. Sustainability is becoming a way to offer differentiation to customers and create new revenue opportunities.

Moving beyond traditional approaches

Traditional device refresh models are no longer enough to keep pace with the rapid advances of the AI world. It’s all too easy for organizations to find themselves either falling behind their competitors or over-investing early in the wrong technology. IT environments are becoming more complex, with hybrid cloud, AI, edge computing, and distributed workforces ripping up the rulebook of how organizations operate.

Many customers need a helping hand to stride confidently into this new world. This has raised expectations for partners who are now expected to deliver across the full lifecycle, from design to an ongoing process of optimization. This requires a new way of thinking from partners, and new, higher-value ways of doing business.

Service-led approaches

Services are the foundation of sustainable business growth. Service-led approaches to device strategy offer an opportunity for channel partners to move beyond simple hardware sales and towards a more consultative relationship with customers. For years, success in the IT channel has been driven by one simple metric: product volume. Today, customers have moved beyond buying devices or infrastructure; they are looking for solutions that deliver across the technology lifecycle, driving business value over the long term.

With partners offering advice on issues such as AI readiness, lifecycle management, sustainability, and long-term workplace transformation, service-led models increase customer lifetime value and build deeper relationships. What’s required is a shift towards lifecycle thinking. Customers are no longer focused on individual transactions; they are taking a broader view of how technology performs over time, from deployment to support to refresh and recycling. For partners, this is an opportunity to engage earlier, stay engaged longer, and deliver continuous value.

Operationalizing AI

This is also an opportunity for partners to take a consultative role in helping customers to operationalize AI effectively. For partners, this means they may need new skills and new delivery models. Expectations are rising, and customers need faster deployment and proven expertise. For partners, the ability to deliver outcomes is becoming a key differentiator.

At present, there is a clear gap: many partners understand where the market is going but are not yet fully equipped to really capitalize on it. But some partners are already succeeding, evolving into trusted advisors, helping customers navigate complexity.

Successful partners are not replacing their product business but attaching services and expanding into higher-value solutions. By building capabilities in everything from managed services to AI and digital workplace, partners can establish more resilient, scalable business models. By doing so, they can build relationships that extend beyond a single transaction.

The road ahead

Enterprises have much to learn when it comes to AI device refreshes. Traditional approaches are no longer working, with many enterprises misunderstanding the impact of AI on everything from sustainability to employee experience. This is also changing the role of partners, who are now taking a more consultative role in everything from sustainability to driving business growth.

Looking forward, this shift, driven by AI refresh cycles, means that the future of the channel will be defined by services. Partners who make the shift towards offering services as a layer that connects product to business outcomes will be better positioned to build longer-lasting customer relationships and thrive in an outcome-driven market. Growth will be delivered by the value partners deliver every day, across the whole lifecycle.

Fears that AI could escape human control have heightened calls for a ‘kill switch’, which would require companies developing frontier models to cut off access to their tools.

Politicians in the US have been putting forward a series of legislative proposals in an attempt to rein in AI models that could go rogue.

The AI Kill Switch Act was introduced in late July with a view to forcing developers to throttle, suspend, or shut down AI systems at risk of causing catastrophic damage.

“As a computer science major, I am very aware of the dramatic possibilities – both good and bad – that AI presents,” Democratic congressman Ted Lieu said in a press release. The bill had the support of both parties.

The Stop Rogue AI Act, another bipartisan bill, was introduced on September 15. It would guarantee the US National Institute of Standards and Technology develops standards, guidelines, and best practices for controlling AI agents

“Right now, AI agents are running loose in our networks, and nobody can see them or verify who built them, making it increasingly hard to stop them,” Democratic congressman Josh Gottheimer said in a press release.

An AI Emergency Button Act was introduced by Republican congressman Tom Kean Jr on September 24. “While AI can be a helpful tool, it is essential that humans remain in control of complex artificial intelligence systems,” he said in a press release.

Legislation is already facing hurdles, though. An attempt to fast-track the passage of a separate AI Emergency Button Act was blocked by the Senate on September 16. It could be months before President Trump actually signs a bill into law.

The operational risks of a ‘kill switch’

While legislation is well-intentioned, there is an argument to be made that any ‘kill switch’ would be difficult to implement from a frontier model perspective.

“If something goes wrong, for example, an AI attack on national infrastructure, you’d want to be able to roll it back and switch it off. In reality, though, AI exists at every level. It’s on our mobile devices, across physical networks, in data centre networks, on our laptops. You'd have to find all of it before you could shut it down,” Charlotte Wilson, head of enterprise at Check Point, tells ITPro.

Gary Barlet, public sector CTO at Illumio, adds that a ‘kill switch’ could cause systemic risk given how embedded AI has become in business operations.

“If a model suddenly becomes unavailable, those workflows could be disrupted overnight. Even intended as a safety measure, it could become a source of uncertainty, disruption, and economic harm if it's triggered too aggressively or without clear standards,” Barlet says.

Darren Guccione, CEO and co-founder of Keeper Security, points out that any ‘kill switch’ would not make AI secure by itself and definitely wouldn’t address every risk. However, it would help to establish an emergency baseline that frontier model developers have to meet.

Preparing for a shutdown

The question for organizations is whether they’re prepared for developers having to design a ‘kill switch’ into their frontier models – and how they would react to it.

Guccione says that large companies generally have a bigger pool of resources to invest in a wide range of models. However, it’s the smaller companies that are likely to be dependent on the same frontier models, putting their operations at greater risk of being impacted by any switch-off.

Smaller companies are also less likely to invest in their own AI safety mechanisms. Kiteworks’s annual data security and compliance risk report, published in July, found that the ‘kill switch’ gap is widening. Researchers at the software development company had forecast that 60% of organizations in 2026 wouldn’t have a tested capability that can shut down an active AI agent in real time, but the actual figure was 79% among 459 security and compliance professionals surveyed.

In the event a shutdown suspends a model that workflows are built around, the onus is on organizations to keep whatever depends on the model functioning, Guccione stresses. “Continuity planning needs to treat that as an operational imperative in its own right."

It may seem obvious, but to prepare for a shutdown, organizations need to improve their visibility by establishing an inventory of AI models and their dependencies.

“They should identify where AI models are embedded in business-critical processes, which providers those processes rely on, and what data and systems are connected to them,” Guccione continues.

From there, organizations need to put a fallback plan in place for their most important business processes. Where possible, they should “avoid unnecessary dependence on any single provider” and seek out alternative models to prevent single-model lock-in, Barlet advises.

A ‘kill switch’ would increase confidence in AI

While unexpected shutdowns caused by a ‘kill switch’ could scare organizations into stopping using models, its introduction would likely drive AI adoption in the long run.

As Barlet explains: “There's a tendency to assume regulation slows adoption, but uncertainty is often the bigger obstacle. When organizations understand the security requirements, testing standards, and governance obligations, they're more willing to invest. The right safeguards can increase confidence.”

Ultimately, a ‘kill switch’ would mean that organizations don’t have to rely on vendor assurances about AI safety, Guccione adds. “It would shift trust from claims made by individual vendors towards controls that organizations can actually verify.”

As we move further into autumn, conferences start to come thick and fast. This week was NetApp's annual Insight event, live from Las Vegas, where there was another chance to hear more about how smart storage, AI factories, and data readiness can empower enterprises.

However, it's been a challenging year for NetApp, with growing concern over the ever-rising cost of hardware and the rapid expansion of agentic AI pulling safety concerns into focus.

ITPro's news and analysis editor, Ross Kelly, joined the pod live from an unusually rainy Las Vegas to talk through NetApp's announcements.

Highlights

"I will say, anecdotally, I do think that storage is becoming the next big talking point when it comes to AI. We've gone from talking about GPUs; we've spoken about latency – latency is part of the story when it comes to storage – as we move away from training to inference, storage becomes a more important part of the AI infrastructure."

"The company appears very confident that this is going to be a key differentiator from their perspective; Keystone is a storage as a services offering and offers a degree of flexibility and scalability for customers, and adding that sovereign add-on is a big lure for companies."

Links

• NetApp CEO George Kurian: AI adoption
• I can't believe we're still talking about AI data readiness
• NetApp Novus has AI factory bottlenecks in the crosshairs

Data readiness is by no means a new talking point within the tech industry. Travel back a decade and IT leaders were grappling with how to capitalize on the data goldmines (or oil wells) hidden within their infrastructure.

AI, naturally, has thrust this topic back into the spotlight. Data is the lifeblood that feeds your various chatbots, assistants, and – now – agents. As the saying goes, ‘garbage in, garbage out’, and IT leaders are very much aware of this.

Despite the fact it’s nearly four years since the advent of generative AI, vendors, providers, and consultants are still trying to drill home the importance of data quality and preparation.

NetApp ranks among the countless companies that appear to be screaming into the void and data readiness was a key talking point during my time at NetApp Insight 2026 this week.

During the opening keynote, CEO George Kurian emphasized the importance of data readiness for AI. Naturally he also took the opportunity to highlight how the company and its products can help, but it wouldn’t be much of a company conference without that.

As I noted in June this year, Everpure has undergone a transformation over the last two years. The all-flash provider is focusing heavily on data management capabilities for customers, launching its Data Intelligence platform.

That system aims to help enterprises maximize the use of AI-ready data and break down the pesky silos hampering AI adoption.

NetApp is no different in this regard – indeed, it’s been somewhat ahead of the curve. Over the years, the company has banged the drum for its ‘intelligent data infrastructure’ and unified storage capabilities.

Its Ontap storage operating system is chock full of data management features and tools, while the NetApp Console has also received a sprucing up of late. The management interface now comes equipped with a range of AI features, as I reported from the conference this week.

One can’t fault NetApp or its counterparts in the industry for pushing heavily on this front. It’s the way the market’s going and vendors can no longer remain as pure play hardware shops.

The fact that they’re still repeatedly highlighting the importance of these tools suggests that the messaging still hasn’t quite hit home, though.

Speaking to ITPro at Insight ‘26, Kurian noted that AI adoption is still fundamentally a business and leadership transformation program - and he’s not wrong. No amount of shiny new tools and promises will whip enterprises into shape unless they realize there’s now an urgency with AI adoption.

Failure to keep pace with competitors and an inability to deliver successful adoption programs will ultimately impact their bottom line. But it’s not like they’re unaware of this.

A slew of studies over the last three years have highlighted the challenges of pushing AI projects from pilot to production. The situation is exacerbated in the agentic AI space, as we’ve reported several times at ITPro. At this point, it’s almost boring seeing yet another piece of research on this topic.

Analysis from IDC shows that data quality is a key factor in this regard, with more than half (52%) of companies identifying this as the “most important factor” when achieving success with AI.

NetApp, to its credit, has equipped enterprises with the tools and infrastructure needed to help overcome this challenge, as have competitors in the broader cloud storage market.

They’re ultimately still beholden to the apparently sluggish progress many enterprises are making though and I suspect there may be a lingering sense of frustration over this.

Tackling the bottleneck

Enterprise data quality is just one part of the equation here, though. As we learned at Insight ‘26, traditional storage architectures are cracking under the pressure of AI.

Storage infrastructure simply can’t contend with increasingly eye-watering AI-related compute and networking performance requirements.

AI factories are growing in size, scale, and intensity, and no longer house several thousand GPUs. Indeed, sprawling gigafactories are targeting clusters of around 50,000 GPUs, even 100,000 in the coming years, according to NetApp.

Put simply, these factories will grind to a near halt unless the storage systems underpinning them can provide a continuous stream of data used in AI training or inference.

The resulting bottleneck is a huge problem that means AI projects can falter and collapse. Worse still, it means organizations are effectively torching large sums of cash while GPUs sit idle.

During the day-two keynote, NetApp’s chief platform and technology officer, Arindam Banerjee, noted that a stalled cluster of 100,000 GPUs could cost “tens of millions of dollars every day”.

NetApp’s answer to this lies in Novus, a new storage architecture designed specifically for large-scale AI factories and GPU clusters. Kurian described this as one of the most exciting announcements ever made by the storage firm, and I must say it’s not hard to see why.

This zettabyte-scale system is capable of delivering up to 100Tbps of throughput. In one fell swoop, NetApp could have solved those recurring AI bottlenecks hampering AI innovation.

Throughout the event, I noticed a palpable sense of excitement around Novus, and for NetApp this could be a huge draw in the coming months and years. The company is actively targeting neoclouds, an emerging area into which huge sums of money is flowing.

It’s a highly lucrative market, and if NetApp can nail the execution then there’s a real chance for it to differentiate itself in an increasingly homogenous industry.

While this is a huge source of excitement for NetApp, it’s worth noting that this addresses problems at the upper end of the scale. For your average business, running clusters of several thousand GPUs is a mere pipe dream.

IT leaders will still be grappling with basic data preparation and quality issues. Pilots will continue failing, and I suspect in a year’s time NetApp will still be talking about the importance of AI-ready data.

I, once again, will likely be left wondering why we’re still talking about it.

A deluge of alerts combined with escalating cyber threats is challenging modern security operations centers (SOCs). Yet there is a huge gap between the best and worst organizations’ SOCs, according to a recent advisory from the US Cybersecurity and Infrastructure Security Agency (CISA).

The agency has illustrated its findings after conducting red team assessments at two organizations, which led to very different results. While both were compromised as part of the tests, one failed to detect or contain the activity, but the other quickly identified initial compromise attempts, isolated affected systems, and forced the red team into an ‘assume breach’ model.

The detection and response gap between the two organizations “had nothing to do with tools or maturity”, says Crystal Morin, senior cybersecurity strategist at Sysdig. “Both organizations faced comparable attacks and triggered similar alerts, but organization A lost them in a sea of noise.”

In contrast, organization B’s fine-tuned system cut through the noise to allow analysts to spot and act on anomalous activity in minutes. “Ultimately, organization B was able to stop the red team activity because it had the right approach to baselines and triage,” Morin tells IT Pro. “It knew what good behavior looks like, and what steps are out of line.”

Halting attacks

CISA’s test shows that a robust SOC can help halt an attack even after an adversary has compromised an organization.

Martin Riley, CTO at Bridewell, describes how organization B, operating in the water sector, detected each payload from the phishing emails as it executed and isolated the affected workstations, taking up to 20 minutes to contain the threat. “Command and control was also disrupted before the intrusion could move further,” says Riley.

It comes at a time when SOCs are drowning in alerts, making it challenging to keep up. Security analysts are forced to spend 68% of their day on reactive alert triage and manual data gathering, leaving little time for proactive threat hunting, according to a new report by ExtraHop.

As CISA’s report reveals, modern SOCs are learning that traditional network monitoring and high alert velocity can “quickly become overwhelming” for human analysts and security teams, says Debby Briggs, vice president and chief information security officer at NETSCOUT.

CISA’s tests revealed that organization A’s SOC suffered from alert fatigue, with a high volume of either false positive or low-value alerts triggered by normal business operations. “This is a relatively common SOC issue and results in a signal-to-noise problem, where high-value alerts get drowned out and go unnoticed,” says Chris Oakley, SVP Assurance Services, Americas, LRQA.

In one instance, an alert triggered by the red team was closed as a false positive because no one in the SOC could work out who owned the affected server. This is “a common story of technology not being the limiting factor”, says Oakley.

Robust detection engineering practices combat this, but it’s “not glamorous work” and “requires constant iteration”, he says.

A lot of the weaknesses CISA found are familiar, according to Cyrille Badeau, VP, EMEA, at Securonix. “Some controls were in place, but they were not configured or tuned well enough to help the SOC at the point of investigation. Environments get larger, new applications are added, people change roles, and service accounts stay in place for years. The security team ends up looking at activity without always knowing what normal behavior should look like for that account or system.”

Tool sprawl adds “another level of complexity”, according to Badeau.

Lessons for all

The two organizations tested by CISA were operating in critical sectors. However, the report’s findings are applicable to SOCs more widely, experts say.

Learning from the findings, Oakley highlights some “widely applicable quick wins”, such as ensuring cloud keys have expiry dates, checking cloud application permissions, and removing passwords from text files. “These things don’t necessarily need technology or a sizable budget and are achievable through time and mandate.”

The operational lessons can apply to any organization’s network, according to Briggs. She believes firms should consider transitioning from manual triage to learning from past incident resolution patterns and automating incident response tasks.

Most importantly, the right metrics must be prioritized, says Oakley. “Alert volume and ticket count pale in comparison to mean time to containment, in terms of real-world efficacy. Beyond that, having a unified area of visibility across areas such as endpoints, identity and cloud is key, especially with identity becoming the primary attack surface.”

At the same time, analysts must have authority to act, says Oakley. “It’s no good having a team who can tell you something bad is happening but are unable to do anything about it."

Oakley also advocates “solid fundamentals” such as detection engineering to reduce alert fatigue and “other human failures”. The ideal SOC is “set up in a way that harmonizes people, process, and technology”, he says. “Tooling is rarely the primary weakness in a SOC.”

Automation also earns its place in the SOC. “As CISA noted, it speeds up token revocation, because manual processes are too slow when a threat is active,” says Morin. “It also makes just-in-time access reviews practical, so excessive access like administrative rights can be removed or re-provisioned automatically.”

However, while useful, she points out that automation cannot replace human investigative instinct. “No amount of automation or tooling will understand when something isn’t quite right, and you need to dig a little deeper, as organization B did after cutting off the red team’s command and control infrastructure.”

At the same time, firms need to assume that whatever is working today will always require tuning, according to Badeau. “Cloud estates, identities and applications change, and attackers adapt. Regular scheduled exercises give teams a way to see where detections have weakened, where context is missing, and where response has become slower,” he says.

“No SOC is future-proof, but a good SOC should be able to keep learning its own environment and adjust as threats change.”

Nearly all of the identity and fraud control systems companies rely on were built with a human at the centre of the transaction. Verify the person, check the document, run the liveness test. That model is now quietly becoming outdated. Visa, Mastercard, and PayPal are investing heavily in AI-powered commerce capabilities and have completed thousands of transactions.

Businesses are authorising agents to book, buy and transact on their behalf - and the compliance infrastructure underneath most of them was never designed for a customer that isn't a person. The scenario channel partners should be putting to their clients is one many haven't considered yet: what happens when your customer isn’t a human?

Agents don't have passports

Traditional KYC and AML were built around two things a human brings to onboarding: a document and a face. Check the passport, then a selfie or liveness test confirms the person holding it is real and present. An agent has neither - it carries no passport, fails every biometric check by definition, and can't be re-verified mid-transaction because there's no one there to re-verify.

But the deeper problem is ongoing due diligence. Regulation doesn't ask for a one-time check; it asks for continuous monitoring. Most businesses are now retrofitting trust, identity, and fraud controls into systems originally built to keep automation out, and the result is a worse experience for everyone moving through them. In agentic commerce, you don't even get the usual complaints. The only feedback is lost revenue.

Recalibrating behavioural analytics for bot traffic only addresses part of the problem. A business can stop treating automated traffic as hostile by default, adjust the rules around session velocity, device consistency, and transaction cadence, and learn to tell a legitimate purchasing agent from a credential-stuffing script. But that only answers a behavioural question: Does this activity look suspicious? It doesn't answer the compliance one: is this agent authorised to act, on behalf of whom, under what permissions, and are those permissions even still valid?

Identity should never be treated as static. Names and addresses change. Immigration, residency, and professional status can be granted or withdrawn. Real ongoing monitoring involves businesses continuously asking whether the identity, risk profile, authority, and credentials tied to a customer are still valid at the moment you're relying on them.

Agents sit between the customer and the transaction, carrying what is effectively the badge of the person who sent them. A customer lets an AI agent buy software on their behalf. The agent buys, the payment clears, the behavioural data is clean - on every signal traditional monitoring tracks, this is a good transaction. The business still has no idea who actually authorised the agent, what it was allowed to buy, whether that authority still holds, or whether it has drifted out of scope and is renewing licences nobody approved. The question shifts from 'who is the customer?' to 'whose authority is this agent carrying, and is that authority still real?'

Scale that up, and you're not dealing with one bot you can label good or bad - you're dealing with thousands of them, acting across channels on behalf of different people, companies, wallets, and platforms. The most mature businesses in BFSI are already worried about this, but most SMEs haven’t even started having this conversation.

The channel opportunity: trust infrastructure, not tooling

This is where channel partners are well placed, but only if they change the framing from "which tools do you need?" to "how does your trust infrastructure need to evolve?" Most clients aren't thinking about agentic commerce as a compliance problem at all; they're thinking about AI agents as a productivity win. A channel partner's job is to make sure that as clients move faster, they don't quietly create compliance exposure.

So the conversation that needs to happen now isn't only about detecting automated traffic. Clients need help deciding which automated interactions can be trusted, what evidence is required to support that trust, and how delegated authority is monitored over time. Start by mapping the controls a client already has for bot traffic, then help them capture the commercial upside of agent-driven traffic while reducing the exposure that comes from accepting it blindly.

The businesses doing this well have already moved from siloed, event-based verification to a continuous model that handles humans, devices, and agents together. The partners who understand this will become trusted advisors; those who don't will be stuck reacting rather than anticipating client compliance failures.

The window to act is now

Companies don't need another isolated protocol, another fraud rule, or another bot-traffic indicator to tackle this challenge. They need help stitching the pieces they already have into a trust framework built for agent-initiated transactions: connecting KYC and KYB to delegated permissions, linking wallets and credentials to ongoing monitoring, making sure credentials can expire, be suspended or revoked, and treating agent activity as part of the customer risk lifecycle rather than a one-off access-management event.

The frameworks most businesses rely on were built for a different era. That doesn't make them wrong; it makes them incomplete. Getting ahead of this is still a choice, for now.

Businesses face a multitude of risks on a daily basis, from financial and operational to technical, reputational, and strategic. Risk management allows organizations to identify, assess, and mitigate these threats, giving teams the ability to minimize the likelihood they will occur. It also enables them to plan how they will handle them if they arise.

Enterprises typically follow deterministic modeling practices in risk management, which correlate to fixed inputs, single-point estimates, and predefined scenarios. Deterministic risk modelling uses input features to predict a single definite outcome; for example, estimating revenue based on a fixed growth rate or planning a project timeline based on previous projects.

The problem with a deterministic risk model is it ignores changing conditions. Put simply, they’re imperfect. “All models are wrong”, said English statistician George E P Box when sharing model limitations in 1976. Any failure in risk management may lead to unexpected losses, downtime, or even corporate failure.

The real world is fluctuating and dynamic: risks are interdependent, nonlinear, and evolve continuously, where output no longer depends on input values. Enterprises are considering iterative models to handle risks and in model planning, and tend to ignore probability distribution. That’s where probabilistic models come into play.

What is probabilistic programming?

The main function of probabilistic programming languages (PPLs) is to enable statistical modeling.

Most probabilistic programming languages are extensions of general-purpose programming languages with the rand () function and all the necessary tools to perform specialized statistical modeling. Sometimes, probabilistic programming languages can be standalone, for example:

• PRISM extended from B-Prolog
• Hakaru extended from Haskell
• bayesloop extended from Python
• ProBT extended from C++ and Python
• Standalone Church developed at MIT

Probabilistic programming languages are predicated on Bayes' theorem, an iterative process that updates or changes with respect to incoming evidence. The arrival of data at every step pushes the posterior update. This may seem dense, but probabilistic programming will become even more relevant in the coming years, to bridge the gap between outputs from quantum computers and traditional compute architectures.

Use cases in enterprise risk modeling

Based on a deterministic risk management model, enterprises weigh every object or event equally on similar checklists.

Supply chain risk modelling

According to an insight by Gartner, IT decision-makers must incorporate probabilistic approaches to adapt and recover during supply chain disruptions.

Supplier data metrics, such as performance, price justification, and ROI, update the model continuously to predict risks. Some suppliers might be more exposed to geopolitical factors that could disrupt their operations. Other suppliers might continue to operate, although their rate of output could be affected. As a result, enterprise resource planners can assess vendor relationships throughout the year.

Workplace health risk modelling

In factories and chemical industries, where on-site safety is crucial, probabilistic programming can be used for workplace health risk modelling. Enterprises define deterministic threshold levels as part of general analysis but each human is unique and will respond differently. This represents a gap in the data. Probabilistic health and safety management suggests risk concentration and forward-looking liability estimates.

Clinical trial risk modeling: Clinical trials are completed in three phases that cost billions of dollars. Based on historical data, cardiovascular drugs pass around at 12%, while Alzheimer’s drugs incur a high failure rate of 99%. Pharmaceutical companies running probabilistic models can save money by dropping trials when posterior efficacy gets too low.

Financial risk management: Probabilistic credit assessment deduces a credit score as a function of probabilities over a predefined range rather than a single deterministic number. JP Morgan is already using Bayesian inference for robust earnings estimates in financial risk management. Catastrophe and actuarial modeling in insurance markets focuses on updating posterior tables to formulate rates.

Cybersecurity risk modeling: Factor analysis of information risk (FAIR) is a framework that measures security and operational risks as quantitative, enabling enterprises to measure risks in financial terms (numbers). Probabilistic programming can share loss linked to an enterprise asset or process in dollars, rather than a qualitative result for best or worst-case scenarios.

When will probabilistic programming be mainstream?

Probabilistic programming significantly reduces the amount of training data, model development time, computation costs, and power budgets required to produce rich results. Nevertheless, deterministic models are still the most widespread when it comes to risk modelling and planning. Probabilistic programming is starting to make its way into enterprises, as seen by JP Morgan using Bayesian inference in its financial risk models – but the process is slow.

Probabilistic programming is used in research or complex fields due to its computational intensity, slow inference speed, and complex structure. It’s also very difficult to validate and debug such models. This means that, while they may be more dynamic and responsive to real world issues, they could ultimately be more effort to maintain.

Proposed capital expenditure from big tech companies into AI infrastructure has been astronomical. The likes of Meta, Google, and Microsoft, among other hyperscalers, are leading the charge on emptying their coffers to spend on data centers not just in the United States, but across the world. Among the beneficiaries of this spending are neoclouds, gobbling this cash up as part of their ongoing infrastructure buildout, alongside borrowing and share issuances.

But all this infrastructure spending is not simply numbers on a screen or figures to trawl through on an Excel spreadsheet. It's required spending, experts say, for the AI industry to sufficiently build the systems we need in the future – whether that might be smarter models, agentic clusters or simply to provide more capacity as more and more users start plugging into a new wave of AI-enabled productivity tools. And, at the other end of the dollars spent, are people who live in areas affected massively by the presence of these ever-humming, environmentally-questionable monuments of silicon and steel.

That's why, in many parts of the US and elsewhere, severe public backlash has spilled into regulatory action – which ranges from more consultation with local communities to a moratorium on construction. Is there anything that businesses can do to move past this movement?

Explaining the big AI backlash

Amid a proposed piece of federal legislation that seeks a national moratorium on building or upgrading AI data centers until safety is mandated by law, government at the state level has issued several banning orders against new data center construction. This includes New York State, Texas, Maryland, and Pennsylvania, among others. So where does this growing sentiment against AI come from, realistically? Much of it has to do with the impact on power and the environment.

"There is a trend right now of going from local bans and moratoriums to statewide ones," explains Arif Gasilov, partner at energy and environmental consultancy Gasilov Group. "New York enacted the first statewide moratorium in July, and in just one week in September, the governors of New Hampshire, Connecticut, and Oregon all announced pauses or refusals.

"In addition, moratoriums that are countywide still copy each other; more than half of Maryland's 24 counties now have one. In addition, the costs of data centers in local counties, such as electricity bill increases and whatnot, show up for everyone, while tax revenue shows up only to the local government and jobs only to the local skilled electricians, and so on. Overall, as such, the overall negative sentiment gets spread across more people."

This is a sentiment that Nicholas Nadeau, co-founder and CTO of Onix, echoes, telling ITPro that "most of the concerns are real".

"Underneath the noise and water complaints is an ownership question. People already paid for this industry in three currencies: their work that became training data, their wages that never moved with the productivity gains, and their savings that index funds route in at valuations insiders set. Data centers add a fourth, and it is the visible one: their grid, their water, their land."

The rising cost of electricity is the biggest factor that's impacting public mood, as well as the impact on water usage in arid areas – with evaporative cooling using the same supply that residents are under restrictions for. For example, a Meta facility in Newton County, Georgia, uses 10% of the entire county's supply. When you live nearby, there's a need to rely on diesel-based backup generators, traffic due to the construction, and a reduction in the local housing supply too, among other knock-on effects.

Dan Ye, CEO of CollegeNode and UC Merced lecturer (and former Johns Hopkins University lecturer) in AI, blames some, but not all, tech companies. "A handful of bad actors have done real damage here – xAI's Colossus data center in Memphis, Tennessee, is the most cited example, generating significant noise and discharge complaints – and the media coverage that follows fuels fear among residents elsewhere who are simply weighing whether to welcome a data center into their neighborhood."

He adds that a data center done right has "minimal impact" on neighbors. "These are engineering and siting choices, not inherent features of the technology – which is why it's worth being careful not to throw the baby out with the bathwater," he explains. "Data centers are also a significant source of local revenue."

What does this movement mean for AI progress?

Frontier training could slow down significantly if there is a sustained shortage of computing capacity, Ye says, while limiting how AI services are deployed and keeping usage costs higher than they'd be with more available infrastructure. But temporary pauses may not make much of a difference at all in Nadeau's eyes.

"Arvind Narayanan ran the arithmetic on Hard Fork podcast last month and found a one-year state moratorium delays AI progress by hours, not months, because efficiency at a given capability level improves several times a year while new capacity adds roughly a quarter more, and blocked sites get rebuilt elsewhere anyway," he says.

There's also a distinction to make between calls for a slowdown in the infrastructure and calls for a slowdown on the safety and security front. This is something that Ye characterizes as two pressures pulling in different directions. If political pressure persists, he says, tech companies will push data centers offshore to places like Canada, given its abundant hydro power, or even China, which is enduring a renewables boom presently.

The pausing for safety rhetoric, meanwhile, is just cover, according to Gasilov, "because it made them look responsible while the real delay, as per the data, is grid queues". Indeed, there's a massive backlog when it comes to the power generation and infrastructure, with many sites not expected to go live until 2027 or 2028. These sites will largely build their own behind-the-meter gas generation plants to power them, sidestepping both grid connection delays and protests, but may result in much higher rates of air pollution and premature deaths, he warns.

Can the industry do anything to ease concerns and get back on track?

Almost half of the states in the US have approved large load tariffs, which make data centers pay their own grid costs for upgrades to the grid – and more than ten states have filed moratorium bills. Gasilov says that the opposition argument wins out at county planning meetings, but won't necessarily solve the problems with bills, for instance.

Companies in this space can do several things to ease concerns and smooth the resumption or acceleration of this massive infrastructure buildout, the experts tell ITPro. Ye persuades site developers to bake community acceptance and deliverable power into the investment case from day one and not treat this as an afterthought. As he puts it: "Evaluate sites against actual grid capacity, water availability, residential proximity, and how much development is already planned nearby. A cheap parcel can turn very expensive once you factor in years of interconnection queues and approval delays."

As for businesses that buy AI services, he says a reasonable practical response is to understand a supplier's capacity constraints and lock in a service commitment that matches up with your organization's particular needs. He advises that critical workloads should remain portable, if possible, and they should invest in smaller models that can run either on-site or even on-device rather than chucking everything into the cloud. Nadeau puts it more bluntly, saying: "Stop naively buying compute by the token." He adds that businesses should measure cost and energy per completed task, then size the model to the job, while pushing AI inference as close to the edge as possible – cutting down on latency and compute costs while also limiting privacy liability in the process.

Ultimately, however, the buck stops with the infrastructure developers and city planners. Microsoft, for example, set a very useful benchmark with its commitment in January 2026 to cover power costs and water use, Ye says, adding: "The strongest reassurance is a project residents can actually hold accountable. If the economics only work by shifting infrastructure costs or environmental burdens onto neighbors, the business model itself needs to change."

Leave it to the Office for National Statistics (ONS) to give the UK’s AI ambitions a reality check. In a July data release, the UK stats body found that take-up of AI tools among British businesses has risen a meagre 0.2% since 2023. Between now and then there’s been significant advances in the reliability of LLM outputs and the protocols that hook models into the apps and platforms that hold business data. It would therefore be fair to argue that AI adoption among UK firms hasn’t yet reflected these advances, and a major uptick is on the cards.

That’s the thinking of many UK channel partners. In shallow adoption of AI lie opportunities to consult many new customers through the process of entrenching AI into core operations. At the current moment, UK business leaders are trying to make sense of new agentic AI products from AI labs and best practices for their rollout. Channel partners can help, acting as the connective tissue between platforms and customer intent.

‘Can’ is the crucial word. While partners possess the technical and change management skills to rise to the moment – a deliberate shift in partner skills and proposition framing is also required. Deliberate data and analytics advisory has the right answers to break businesses out of stop-start AI pilot stasis. Partners need to position themselves to hold those answers.

The ambition-execution gap

The state of the UK’s AI rollout is similar to other countries in that it’s plagued by a failure to scale beyond pilots. Indeed, in February our research found that more than 80% of AI projects fail to operationalise into production.

The bottleneck is not AI access (which is increasingly commoditized), but what AI systems are built with. Fragmented, and often inaccurate, data spread across business units is the norm for many UK firms. Business logic - the rules, calculations, and decision-making definitions that determine how work gets done - is locked in spreadsheets, legacy systems, and employees' heads.

There’s a clear risk of deploying AI over broken data foundations, a risk that compounds as the application of AI gets more sophisticated - like the deployment of AI agents.

This is not a problem IT can fix in isolation. Reckoning with this state of affairs requires honesty on the operating system refresh required to make AI work. Wrangling with data is one aspect - but so too is addressing the trust and culture issues that slow AI rollouts down. The conundrum is a business transformation challenge, which requires both technical capability and contextual advisory. That is exactly where channel partners are comfortable.

How channel partners can think about the operating system refresh

For many organizations, AI is seen as a line-item purchase. Business users are set up on the software with perhaps some training. The conditions aren’t set for pilot testing of AI to scale up into a transformational reimagining of business processes.

Channel partners can address that gap with a neat proposition: a pre-agent layer. This is an analytics platform that sits underneath AI systems with pre-processed proprietary data, defined rules, and business context. Models connected to the layer are rooted in how the business actually runs and, therefore, more easily integrated into day-to-day work.

Channel partners should know that such propositions are most effective when built around platforms that offer visual-in-nature, intuitive-to-build analytics workflows that make AI visible, understandable, repeatable, and auditable.

Managers can quickly establish the inputs into AI workflows and validate the presence of governance steps and role-based access controls to extract the confidence they need to sign an AI workflow off. With a pre-agent layer proposition, channel partners come with workable solutions to help UK firms evolve beyond AI pilots and the hesitations that fuel the standstill.

Channel partners are well positioned to build pre-agent layers for UK firms as they already sit inside customer data infrastructure. They have the familiarity with these environments - the legacy systems, the data flows, the processes that run under the surface - to hit the ground running in a solution-oriented way.

The new blueprint for channel success

AI services are widely expected to be the single biggest driver of revenue growth for IT service providers over the next 24 months. That is a significant opportunity - but only if partners evolve "AI services" with the wants and needs of customers.

Wrapping a managed service around a generic AI model is not the same as genuine AI delivery. The skillset that generates commercial growth is the ability to translate a customer's business context into governed AI processes. Partners who build that capability can immediately move into selling AI readiness - helping customers make their data and analytics estate fit for agents and their most impactful use cases.

From my conversations with UK MSPs and GSIs, this evolution is already happening. UK partners, in my experience, are showing innovation that - despite the naysayers - surpasses US counterparts. That’s because the deep customer relationships and genuine sector-specific specialisations that make up the UK IT channel’s strengths are precisely the assets that inform the thinking for a successful customer AI operating system refresh.

The gap is the opportunity

The gap between the state of UK AI rollout today and where it could be is not a failure story. It’s an opportunity for the UK channel. The partners who ready their propositions - vendor relationships, skills, service design - will be the ones positioned to benefit.

In the space of a year, searches for the term "AI resilience" have jumped 4,500% in the UK alone. The OpenAI Foundation has committed $25 billion to what it calls “societal resilience in the age of AI” while everyone from startups to IT giants are launching “AI resilience” tools.

Unlike AI safety, which broadly refers to making sure AI systems behave as they're meant to, AI resilience is being used to describe everything from cyber defence and business continuity to governance, infrastructure and software engineering. Ask a handful of vendors or researchers to define it, and you'll get a handful of different answers.

In the process of writing this feature, more than 50 experts put forward their views on the topic, and while the majority framed it from whichever discipline they’re in, unsurprisingly, they also couldn’t entirely agree on what it means – and that’s a problem.

Agentic AI at scale

For much of the last few years, AI adoption has centred on copilots and assistants, what Denis Shilov, CEO and founder of AI safety and alignment platform White Circle, refers to as “the first wave of dumb chatbots.” Today, the next wave centers on agents. Almost two-thirds (65%) of businesses are already using autonomous AI agents across their operations and by 2028, Gartner predicts a third of all enterprise software will feature agentic AI, up from 1% in 2024.

The scale of this adoption, and the scope of tools available, is partly to blame for the disagreement. To a backup and recovery team, AI resilience means restoring a system after an agent corrupts or deletes something it shouldn't have. To a security team, it means treating an agent as a new kind of identity. To a governance team, it means proof for regulators. To an infrastructure team, it means not being trapped by a single model's outage, pricing or roadmap.

“AI is causing an organizational shift that's rapidly dissolving traditional business and data boundaries”, explains Maureen Holland, Director of Product Marketing, Governance, Compliance & Insights at Mimecast.

"It's embedded into everything, moving across business systems faster than any governance function can track. The old way we built resilience into a business no longer maps cleanly onto how risk actually moves through it now."

It's also easy to misdefine AI resilience by what it isn't. It isn't disaster recovery with an AI label attached; an agent doesn't fail the way a server fails; it can complete a task, produce a result, and have gone about it in a way nobody would have approved. It isn't purely a compliance exercise either. A policy document proves intent to govern AI systems, not that a business actually can.

Another factor is that mentions of "resilience" generally are soaring. The term is twice as likely to be mentioned on earnings calls as it was before the pandemic across personal, climate, financial resilience and more. There’s even a World Economic Forum consortium dedicated to resilience, which officially defines it as “the capacity to deal with adversity, withstand disruption, and continuously adapt as crises arise over time.”

This definition gets us part of the way towards defining resilience in the realms of AI, but it still falls short of establishing exactly what businesses are withstanding and adapting to.

Excessive agency

While traditional apps follow a fixed set of instructions, AI agents have what Shilov calls "excessive agency." "They can drift from the user's prompt, navigate through internal information, and access or disclose things they shouldn't. They can run destructive actions, produce sloppy code, and much more." You only need to look at the OpenAI agent that escaped its sandbox and breached Hugging Face; its response team had to reconstruct more than 17,000 events from that one attack.

So it isn't enough to say AI resilience is about preventing agents from misbehaving. It has to assume an agent will eventually act out and be built around what happens next. "Security has long focused on being robust: how can we avoid all breaches?" says Chris Hosking, AI and cloud security expert at SentinelOne. "Resilience is an acknowledgement that while we need strong walls, we also need to be prepared that a breach is going to happen."

“Prevention alone is an increasingly unrealistic strategy,” adds Michael Vallas, global technical principal at Goldilock Secure.

From fix to contain

This is where we start to get closer to a shared definition of AI resilience: businesses shifting from trying to stop failures to being able to act when one inevitably happens.

"AI resilience often gets thrown around by people who mean, vaguely, 'we bought some tools,” according to Tim Freestone, chief strategy officer at Kiteworks. “In practice it's: can you catch it, contain it, and prove afterward what happened?"

"Most enterprises can’t answer that today,” adds Stefan Deiss, co-founder and CEO of The Hashgraph Group.

“They’re deploying powerful autonomous systems with no auditable chain of authority…and have invested heavily in model safety and prompt guardrails with no equivalent of the identity and access controls they already apply to human users."

Research backs this up: 92% of enterprise security leaders don’t have full visibility into their AI agent identities, and only 16% govern that access effectively. This is significant when you also consider that 26% of today’s agents are approving or denying core decisions (15% acting on financial transactions directly) and that 72% of enterprises say their AI agents are operating with “unmanaged risk.” An issue Nick Balnaves, founder of Threada, attributes to teams having spent “too much time watching the final answer and not enough on the action path.”

Critical infrastructure

To move the term, and industry, forward, the experts largely agree that AI resilience needs to be framed as structural, not conceptual. “Enterprises should think about AI systems in the same way they think about critical infrastructure,” according to Tom Pepper, partner at Avella Security and security lead at the UK government's AI Security Institute.

“If an agent can act without a human in the loop, it needs controls equivalent to those protecting a power station or financial system," he says. This can range from real-time behavioral and chain-of-thought monitoring to access controls, AI asset registers and kill switches.

“AI resilience sits at the intersection of cyber security, safety engineering, software assurance and governance,” continues Pepper, and thus needs to be treated accordingly. “It needs to borrow heavily from zero-trust networking, safety-critical systems and secure software engineering, while introducing new disciplines around behavioural monitoring and autonomous system oversight." In short: no single team can own this, and no existing framework covers it.

Two years ago, enterprise leaders were deciding whether to let staff use generative AI. Now they're deciding how much authority to hand to agents. Purpose-built platforms like White Circle are starting to offer a single view of these agents across security, governance and infrastructure; the likes of Druva are building recovery and continuity specifically for AI agents, rather than each discipline working from its own silo. Yet tooling alone isn't enough.

Until businesses start treating AI as infrastructure and not an add-on, Deiss warns, “AI resilience will remain a label rather than a capability."

AI is already changing how Managed Security Service Providers (MSSPs) operate. It has the ability to analyse security telemetry faster than humans, automate repetitive workflows, prioritise alerts, and accelerate investigations at a scale that security teams simply can’t match manually.

This matters a great deal because MSSPs are under increasing commercial and operational pressure. Customers are expecting faster detection and response times, along with stronger resilience and clearer evidence that their provider can keep pace with evolving threats.

The skills gap

At the same time, MSSPs are facing skills shortages, expanding attack surfaces, cost pressure, and growing operational complexity. Many providers are being asked to deliver significantly more output without growing headcount at the same rate. That is why AI adoption inside MSSPs is moving quickly from experimentation to operational deployment.

However, despite the hype surrounding autonomous security operations, AI is not removing the need for skilled cybersecurity professionals. In reality, it is actually making human expertise more important.

The future of cybersecurity is not “human-out-of-the-loop” - it is “human-on-the-loop.” In practice, that means analysts are not manually approving every automated action, but they are setting guardrails, reviewing high-risk decisions, investigating anomalies, and knowing when to escalate or override AI-led workflows. 

AI is great at identifying patterns, correlating data, and handling high-volume analysis. It can reduce noise, speed up investigations and automate initial response actions. But cybersecurity is rarely a purely technical problem. Security incidents are ambiguous, fast-moving, and highly contextual. They involve business risk, incomplete information, and constantly evolving attacker behavior.

While AI will help security teams move faster, it can’t fully understand organizational context, make nuanced judgment calls or take accountability during high-pressure incidents.

Humans and AI working together

“Human-on-the-loop” is even more important when you consider that the attackers themselves are using AI-enabled techniques. Our Cybersecurity Workforce Intelligence Report found that AI penetration testing is now the fourth-highest global cybersecurity training interest among more than 702,000 cybersecurity professionals worldwide.

The report also identified a rapidly growing focus on prompt injection, machine learning model exploitation, and agentic AI hijacking. These are not theoretical concerns. They reflect how quickly AI-related attack surfaces are evolving and how actively the cybersecurity community is adapting to them.

For MSSPs, the reality is that AI systems themselves are becoming part of the attack surface. Security teams are no longer simply protecting infrastructure, endpoints, and cloud environments. They also need to understand how AI systems behave, how they can be manipulated, and where automated systems can fail. This is why human oversight is still essential.

AI can accelerate detection, but experienced analysts are still needed to validate findings, investigate anomalies, identify false positives, challenge assumptions, and intervene when automation makes the wrong decision. Without that oversight, organizations will become dangerously overdependent on systems they do not fully understand or validate.

The threat landscape

Attackers do not behave predictably, and real-world incidents rarely follow neat playbooks. Threat actors adapt quickly, and they exploit gaps between systems and target operational weaknesses that automated tooling on its own may not recognize. So, in the middle of complex incidents, human intuition, communication and decision-making still matter enormously.

Changes in cybersecurity operations are also changing the types of skills MSSPs need within their teams. Hack The Box’s research highlights growing overlap between offensive and defensive skill development. Defensive practitioners are engaging in offensive training, and offensive specialists are also developing defensive capabilities.

This is all part of a wider cybersecurity industry shift toward more integrated operational models where security teams continuously test, validate, and improve resilience rather than operating in isolated silos. In practice, this means MSSPs need to employ professionals who understand both how attacks work and how defensive systems respond under real-world conditions.

The strongest MSSPs are unlikely to be those who simply deploy and operate tools. They will be the ones with analysts who have the skills to work alongside AI systems, validating outputs, interpreting context, and adapting quickly when automated systems fail or threats evolve unexpectedly.

A shift in focus

MSSPs need to think differently about workforce development. The skills challenge is no longer just about hiring more people. It is about building adaptable teams capable of operating effectively in increasingly automated and agentic environments.

As AI agents take on more security tasks, MSSPs will need professionals who understand not just alerts and incidents, but delegation, supervision, and control. This is why continuous, hands-on training remains a protected investment for mature MSSPs, even when budgets are under pressure. 

For MSSPs, this has direct commercial implications. Customers are looking for providers that can demonstrate resilience, operational maturity and practical expertise rather than simply deploying more security tools. As AI becomes part of managed security delivery, customers will increasingly expect MSSPs to prove readiness: how teams perform in realistic scenarios, how AI outputs are validated, and how quickly analysts can intervene when automation gets it wrong.

The strongest MSSPs will not be those that simply add AI features fastest. They will be the ones that combine AI-driven efficiency with skilled human oversight, realistic training, and measurable cyber readiness.

AI will absolutely reshape cybersecurity operations. But the MSSPs that benefit most will be those that use AI to scale efficiency internally while preserving human expertise. They will be the providers whose teams know how to question AI, validate it, and step in when it matters most.

For more than a decade, fiber-to-the-home (FTTH) has been defined by a single contest - speed. Each new generation of Passive Optical Network (PON) technology arrived promising more bandwidth and more headroom, and operators competed accordingly.

But that contest is quietly losing its meaning. There is no obvious "killer app" driving exponential demand, and even bandwidth-hungry use cases - 16K video among them - are increasingly absorbed by better compression.

Consumption is still rising, but the rate of growth has slowed sharply. Remote work, 4K streaming and cloud gaming are now mainstream, yet widely deployed 10 Gigabit Symmetrical Passive Optical Network (XGS-PON) networks already carry far more capacity than most households will ever use. Average peak usage sits at around 5 Mbps, spiking only when a major title like Fortnite or Call of Duty pushes out a large update. Raw speed, in other words, no longer separates one network from another.

That shift is changing the question operators ask. It is no longer "how fast is your fiber?" but "how effectively can you share it?" - and for the channel, that change opens a market that used to be closed.

The limits of competing on speed

The old model of differentiating through ever-higher tiers runs into three walls.

The first is diminishing returns. Once a customer reaches 300–500 Mbps, the jump to 1 or 2 Gbps rarely changes their day-to-day experience, so speed upgrades deliver little retail payoff.

The second is rising deployment costs. Building parallel fiber networks is capital-intensive and duplicative. Overbuilding may satisfy regulators, but it rarely improves how well fiber is actually used.

The third is opportunity cost. Operators locked into their own infrastructure spend their time and budget on civil works rather than on the cloud, content, smart home and enterprise services that customers increasingly value.

With these pressures mounting, the industry is looking for a more sustainable way forward.

What FANS changes

Fixed Access Network Sharing (FANS) offers a way out. It lets multiple operators, wholesale and retail, share the same physical fiber access network. At the same time, each keeps full control of its own service logic, quality of service, traffic management, and customer experience. Retail providers operate over virtualized, isolated slices of shared infrastructure rather than building parallel networks of their own. Wholesale providers, meanwhile, monetize the same fiber many times over, sharply improving how hard each asset works.

Three things make this viable now. Bandwidth has matured, moving the value from throughput to availability, reliability, and quality of experience. Virtualization and software-defined access have reached the scale needed to support programmable sharing that was impractical a decade ago. And, finally, regulators in many markets now actively favor open access to widen coverage and cut duplication.

Why the channel should care

The most interesting part of FANS is who it lets into the market. A model that echoes the way mobile virtual network operators (MVNOs) reshaped mobile - but with far more flexibility - means utilities, mobile-only operators, niche ISPs and other brands can enter FTTH without laying a single strand of fiber.

That lowers the barrier to entry dramatically. It also lowers cost per subscriber, which makes business cases stand up in rural and hard-to-reach areas where standalone builds would never pay back. New entrants get a route to market; wholesalers get new tenants.

It also changes what a wholesaler sells. Instead of dark fiber or basic bitstream, a wholesale provider can offer a service-ready platform on which partners launch differentiated services almost immediately. That is a more valuable proposition than per-line pricing, and it reframes the wholesaler as a platform operator rather than a pipe.

Innovating without waiting for the next PON cycle

Because the access layer is shared and programmable, innovation can happen at the service layer rather than through expensive hardware refreshes. Providers can introduce smart home bundles, home security, SME connectivity, or edge-cloud experiences without waiting for 50G or 100G deployment cycles. The retail experience can evolve even while the underlying access network stays stable, which decouples competitive differentiation from the PON roadmap.

The payoff is better economics across the board: higher return on every home passed, lower operational complexity as each operator manages its own slice independently, and faster time to market for new services.

A reset for the fiber era

None of this happens on its own. Multi-tenant operation at scale depends on a full stack - operational support systems (OSS), software-defined networking (SDN)- based management and control, virtualized broadband network gateway (BNG), and multitenant-capable optical line terminals - working together to deliver true wholesale flexibility. But the strategic direction is clear.

The old playbook - compete on speed, build parallel networks, chase the next PON generation - is giving way to something more collaborative and more efficient. Treating fiber as a shared, service-ready platform rather than a siloed asset lets operators grow faster with less capital, reach more customers, and compete on service instead of infrastructure.

Beyond speed lies shared value. For the channel, that is where the next phase of FTTH opportunity is being built.

The month of September is usually about the first flourishes of Autumn, but thanks to a cohort of AI companies, everyone is seeing red. Multiple reports of rogue AI agents hacking companies across the internet have led to calls to slow down the research and development of the technology.

And, in an even more unbelievable turn of events, Meta is open to unionization across its workforce…

On this week’s episode of the ITPro Podcast, Bobby Hellard, Scott Bekker, and Ross Kelly look at the recent spate of AI agent hacks and Meta’s deal with Prospect.

Highlights

“It’s interesting that the majority of these are from OpenAI – I’m not suggesting that they’re doing anything particularly worse than their counterparts – but the fact that there has been so many from OpenAI, they were really the starting gun on this.”

“Meta was kinda one of those cool companies to work for; it had that startup approach with free perks and stuff, and it did a mass employment thing during the pandemic and COVID. Then suddenly it dropped it all off, and as Scott alluded to, they haven't got a stellar history on employment and their treatment of employees.”

Links

• OpenAI and Anthropic admit rogue AI agents did more than first thought
• OpenAI reveals six more rogue AI incidents
• Spain says it has seen first AI agent hack
• Prospect union strikes deal with Meta to open up access to 5,000 UK staff

There is a misconception that industrial companies aren't ready for the AI age, but working with my customers every day, I can confirm it couldn't be further from the truth. The industrial world is the ideal proving ground for AI tools, and the sector has already seen a plethora of innovation built on real-world business needs, creating tools that have transformed operations from engineering to manufacturing.

No, it is not the ambition which is lacking, or indeed the innovation; it's a simple problem with a simple fix: tools need to be designed with people in mind. The system goes live, adoption data comes in, and it turns out that eighty percent of licensed users logged in once and never returned. That pattern repeats across energy, manufacturing, aviation, and field service. This raises an obvious question: if the tool works, and it saves time, why do usage levels reflect this?

The wrong framing produces the wrong outcome

It is natural that organizations assign AI deployment to the IT team; it seems the right fit. However, AI is more than a standard rollout of technology and requires more than a set go-live date and a few training sessions done online with 50 people reading their emails during the session. To understand the lack of adoption, we need to understand what lies behind it, and in the case of AI, it is often trust. By building that confidence alongside the rollout, the tool earns a permanent place in someone's workflow.

In industrial environments, that trust problem is acute. A frontline engineer in a mission-critical setting isn't going to rely on a system they don't understand, especially one that has been positioned as something that monitors their performance rather than supports it. When employees read AI as a surveillance tool, they disengage and work around it; the feedback loop collapses, and the system stops improving.

Organizations that close that gap treat deployment as a change management project from day one. They ask different questions before go-live: what do employees currently understand about how this system works, how has it been positioned internally, and who is responsible for listening to frontline feedback in the first 90 days and actually acting on it?

Positioning determines whether the tool gets used

In industrial settings, the stakes are higher than almost anywhere else. Think about restoring power when temperatures drop to minus 40, or sequencing safety-critical maintenance tasks under serious time pressure. In those moments, a worker needs accurate, contextual information immediately, and is not going to pause to consult a tool they don't trust.

The language used to introduce an AI agent shapes the behavior that follows. Frame it as a productivity monitor, and people will avoid it, or perform surface compliance without genuine engagement. Frame it instead as a collaborative partner, one that takes the institutional knowledge of twenty experienced engineers and puts it in the palm of a first-year technician at precisely the moment they need it, and the dynamic shifts entirely.

That shift has measurable consequences: employees who see the system as a partner give it better feedback, flag errors, and challenge it when something doesn't make sense. That input makes the system more accurate over time and more useful to the next person who needs it. Humanizing the AI agent is what makes the feedback loop function.

What gets lost when organizations rush

No matter if someone is sitting behind a desk or working out in the field, an employee is acutely aware of board-level pressure to deploy AI. The board is right to be concerned with AI deployment and to push for adoption; however, it's important to factor human nature into AI strategy. Never before has technology moved so quickly, and never before has it been more important for leadership to give people training and time to allow uptake to catch up with what the board wants.

Frontline workers are rarely asked what worries them about a new system, or told what happens to their feedback once they give it. Retirements across energy, manufacturing, and aviation are stripping institutional knowledge from workforces at pace, and AI can put what a 25-year veteran knows into the hands of every worker on the ground, with the guardrails to use it safely. That matters more than most organizations acknowledge. Employees who understand that will engage with the system; those handed a login and a slide deck won't.

The channel opportunity hidden in plain sight

This dynamic does not stop at the customer's four walls. Channel partners have an outsized role in how this plays out, and a significant commercial opportunity that most are not yet fully exploiting. Customers rely on their advisors to decide what technology to buy and how to deploy it well. The partner who leads with change management thinking is having a fundamentally different conversation than one leading with feature sets.

In practice, this means asking different questions earlier in the sales cycle: how did your last major software rollout go, and who owns employee experience in this organization? Those questions get at something a standard discovery call never reaches, and they open the door to positioning change management support as a core part of the engagement rather than an afterthought.

Partners who build this capability earn stickier relationships. A customer who goes through a successful AI deployment, one where frontline workers actually use the system, trust it, and give it the feedback that makes it better over time, has experienced something a competitor's feature list cannot easily displace. That is the kind of customer relationship worth winning in the AI era.

NetApp Insight 2026 is just around the corner, and the cloud storage giant faces a battle to calm customer cost concerns and to differentiate itself in an increasingly uniformed market.

In recent years, the company has sharpened its focus on data management capabilities for customers. Its ‘intelligent data infrastructure’ approach includes a range of analytics tools designed to help enterprises monitor and maximize the use of data across hybrid and multi-cloud environments.

It’s a common recurring theme we’ve seen in this domain over the last three years. Providers such as Dell Technologies have ramped up capabilities on this front while Everpure has gone all-in on data management.

With customers shifting their attention to agentic AI and contending with the significant infrastructure-related pressures associated with the technology, I expect we’ll see much more on this front at the annual conference In Las Vegas next week.

Tokenomics and inference-related concerns continue to plague enterprises, and NetApp will be keen to emphasize the importance of its intelligent data infrastructure strategy and tools such as the NetApp AI Data Engine and ONTAP unified storage capabilities.

NetApp’s July acquisition of data infrastructure firm DataPelago will also feature heavily on this front. The California-based firm’s specialized data processing technology was designed specifically to tackle performance bottlenecks for large-scale analytics projects and AI workloads.

At the time, NetApp said this will deliver marked benefits for customers in terms of infrastructure costs and will give them “full command” of their data. How the company has integrated DataPelago's flagship Nucleus engine, and how this will complement NetApp customers’ agentic strategies will certainly be a key talking point.

Storage costs in the spotlight

That’s only one part of the equation for NetApp, however. As with counterparts in the industry, the storage giant could be facing some frank discussions over rising infrastructure costs.

2026 has been fraught with rising concerns over AI and storage-related costs. As I reported in June, competitor Everpure apologized to customers over price hikes and thanked them for “sticking with us” during the crisis.

NetApp, it seems, finds itself in a similar predicament, having raised prices over the last year. CEO George Kurian acknowledged the issue during its Q2 FY2026 earnings report in February during the height of the crunch, warning that the company will “do so again as needed”.

Kurian noted that closer collaboration with channel partners and candid communication with customers on this front was a key focus for the company.

A key advantage for NetApp lies in its hardware diversification, however. Unlike some all-flash competitors, the company has a broader portfolio that insulates from cost crunches to some extent.

I expect Kurian and NetApp at large to drive home the messaging on this front at the annual event.

Striking while the iron is hot

While calming customer concerns over hardware prices will be a big talking point, I expect NetApp will be keen to strike while the iron is hot on this front. From a financial perspective, the company is performing very well and Kurian will be keen to highlight this performance as a signal of its appeal over competitors.

NetApp recorded record figures in its Q4 and FY2026 earnings report in early September, posting record all-flash revenue of $1.2 billion for Q4 and marking an 18% year-over-year increase.

Public cloud net revenue also reached $182 million for the fourth quarter, marking an 11% year-over-year increase – the latter of these will most certainly be a key talking point for the storage giant.

NetApp has consistently highlighted its deep integration with hyperscalers as a key differentiator in the market. With close ties to AWS, Microsoft Azure, and Google Cloud, the company will be eager to emphasize the importance of these relationships for multi-cloud customers.

I will be live on the ground in Las Vegas for NetApp Insight 2026, from 28-30 September. Keep tabs on all of our news, views, and analysis from the conference on our website or by subscribing to our newsletter.

Huawei is, I was told last week at Connect 2026, a company of engineers run by engineers. Many of the top executives in the company have come up from an engineering background and it feels apparent in the highly technical announcements made at the conference.

Beneath the technological developments and breakthroughs, though, is a portfolio that many enterprise vendors could only dream of. Chips, networking, servers, and storage, Huawei has a finger in every pie (and that’s before we get anywhere near devices and, yes, cars).

Landing a whale of a client with DeepSeek

Huawei pulled off what, at first blush, appears to be something of a coup when it comes to big customers this month. DeepSeek, the open weight model that smashed onto the generative AI scene in 2025, announced it was swapping Nvidia AI chips for Huawei Ascend in its infrastructure.

There’s no doubt that this is a big deal – DeepSeek changed how people understood generative AI models work and how much they cost to run – but there’s another aspect at play.

Due to the US’s ongoing trade restrictions, there’s no fresh supply of advanced chips from Nvidia or AMD going into China, so DeepSeek had no choice but to turn to another supplier. With the US players off the board, the alternatives become quite limited.

Nevertheless, this offers an opportunity for Huawei to show what its data centre hardware can do. It may not find a market for Ascend, OceanStor, Atlas and the rest in the USA but there’s a big wide world out there full of markets that will be interested in a proven alternative.

In fact, there were many customer spokespeople from Asia and Europe speaking at the breakout sessions held throughout the two day conference in Shanghai, including French supermarket Auchan and Turkish investment bank İşbank Türkiye.

The battle of the SuperPods

Without wishing to drive the Nvidia comparisons too hard, it’s worth also looking at Huawei’s pride-of-place AI infrastructure, the SuperPod.

SuperPods are extremely large infrastructure platforms made up of multiple components acting together as a single system. They are designed for demanding AI and high performance computing (HPC) workloads.

When you enter SuperPod into your search engine of choice, you will find one other name making this type of technology and that’s Nvidia. This hasn’t gone unnoticed and many commenters have suggested that Huawei is gunning for Nvidia’s AI throne, both with SuperPods and its latest upcoming Ascend 970 and 980 chips.

Even Nvidia’s CEO, Jensen Huang, has remarked on Huawei’s growing presence in the AI market, saying in 2025 that Huawei’s “presence in AI is growing every single year”. He added that enterprise tech companies elsewhere shouldn’t assume the company won’t be a factor in the AI race.

Huawei isn’t going to take over the world overnight. I noticed that it wasn’t named in the technical specifications of LineShine, for example, and trade restrictions in the US keep it out of a large market. Once you get through the dense technical detail, though, what’s underneath is a capable company that has a real breadth of expertise, deep technical knowledge and a huge commitment to R&D.

Across the markets we work in, NIS2 has moved from a future concern to a present one. It's a single EU directive, but it only takes effect once each country has written it into national law, and member states have moved at very different speeds. In two of them, it is already in force: Germany, since 6th December 2025, and Greece, since November 2024.

Close behind is France, where NIS2 has been folded into a broader resilience law. Ireland has not passed its law yet, but its National Cyber Security Centre is already acting as the regulator.

While these laws take effect on different dates — some already passed, some still to come — the destination is the same: every one of these markets is heading to full NIS2 enforcement.

The channel has spent the past year preparing for this reality, but unfortunately, a lot of that effort has been misplaced. Much of the conversation I hear treats NIS2 explicitly as a sales opportunity — selling compliance, audit, and security products and services.

That instinct isn't wrong: there is a commercial opportunity here. But the full picture is that NIS2 has broader implications for the channel itself, and partners and MSPs need to understand that before they rush to sell. Because NIS2 pulls partners into their customers’ compliance posture and has them assessed as suppliers, your own security is being checked.

The obligation that arrives through procurement

Of course, none of this is new on paper. The supply-chain duty has been in NIS2 since 2022. What's changed is that as each country writes the directive into law, it has gone from text on a page to something your customers must now act on.

Article 21 requires organizations covered by the law to manage the security of their supply chain, including the security of their direct suppliers and service providers. In other words, a company is now responsible for the security of its suppliers, as well as its own. The European Commission has gone further and set out detailed rules on how this should work for digital providers, including Managed Service Providers (MSPs).

So, for the channel, the consequences directly affect you. If your customer is covered by NIS2 — a hospital, an energy company, a bank, a government body, a large manufacturer — its obligations do not end with its own systems. They extend to the suppliers it relies on. The customer now must assess and record the security of those suppliers, and a reseller with access to its systems, or an MSP managing part of its IT, is exactly the kind of supplier the law has in mind. If your customer falls under NIS2, part of that compliance burden now lands on you.

Belgium’s national cybersecurity authority has told any organization that might sit in the supply chain of a covered customer to meet a baseline security standard and has confirmed that a covered customer can require a particular standard from its suppliers through the contract. Irish guidance says the same in commercial language: suppliers of hardware, software and IT services should expect to prove they meet their customers’ NIS2 requirements.

So how might this look? For a partner, your interaction with NIS2 may be in the form of a request from a customer. To meet its own obligations, that customer has to be confident its suppliers are secure, so it starts asking you to prove it. A security clause appears in the contract. The service-level agreement gains tighter terms. You receive a supplier security questionnaire that has to be completed before the deal can close.

None of this stops at the EU's borders. The UK left the EU before NIS2 existed, so while the directive does not bind UK companies directly, the obligation travels through the supply chain regardless. A UK reseller or MSP that supports EU customers, the UK operations of an EU-covered group, or cross-border critical sectors sits in exactly the chain a covered customer must now account for.

Compliance is not just a product the channel can sell

Let’s look at what NIS2 actually asks of the organizations it covers. Most of it concerns how a company governs its own security: the decisions it makes, the processes it follows, and who is accountable for them. These duties sit mainly in two articles. Article 21 is a list of governance and process requirements: managing risk, handling incidents, checking your own suppliers, and being able to prove it. Article 20 makes a company’s senior management — its board or equivalent — responsible for approving and overseeing those measures and says they can be held liable when the company gets it wrong.

For the most critical organizations, enforcement can go as far as temporarily banning a senior manager from their job. In plain terms, the people at the top now carry the compliance risk personally. None of that is simply something a customer can buy off a product catalogue. A customer can own every security and audit product on the list and still fail an assessment, because it cannot show that anyone is accountable for how those products are used.

What the customer needs is help putting together a security setup it can defend to a regulator. They also need accountable suppliers and partners willing to work towards the security of these systems. That is old-school, relationship-building work, and exactly where a distribution partner can help. No single vendor meets NIS2 on its own. It takes a combination of products and the people who can run them – and a distributor that already works across those vendors can point a partner to the right security solutions and specialists.

The partners who sort out their own security first

The partners who do well out of NIS2 will be the ones who accept that they are now part of their customers’ compliance posture, whether they like it or not. Two things follow.

The first relates to your own security. If you are going to be assessed as a supplier, you need everything documented before customers start asking: your controls, how you handle incidents, who your own suppliers are. The partner who can answer quickly is the easy supplier to keep.

The second is about what you sell, and how you sell it. For partners, the conversation that delivers value is no longer “here is a compliance product.” It is “here is how we help you manage this, prove it, and account for the parts that run through us.” For a distributor, this is familiar ground. Our job has always been to do the assessment work, so partners do not have to do all of it themselves: to vet the products we sell our partners and take responsibility for them.

That kind of assurance — being able to vouch for the security of what you supply — is exactly what NIS2 now pushes down the chain. Each supplier must satisfy the customer above it, and it happens through contracts rather than goodwill. Your customers will start asking it of you, the same way you have always been able to ask it of us.

NIS2 was written to push cybersecurity up to the board and to make companies answerable for the security of their suppliers. Trust between a customer and its suppliers now has to be proven and recorded, whereas before it could be assumed. That is more work, and it is also where the opportunity is: the partner who can show its own security is in order and help a customer account for the parts of their systems that run through it, becomes the supplier a procurement team keeps rather than the one it is told to review.

In a market where it is easy to switch distributors or resellers, that is worth a great deal.

The threat from quantum is already on most security leaders’ radars, but experts argue the reality is growing near. Concerningly, the figures show a lack of preparation for quantum capabilities that will give adversaries the ability to break encryption.

Organizations could face a ticking quantum time bomb, according to research from DigiCert, with the vast majority of firms aware that they aren’t properly prepared. While an overwhelming 85% of IT and security leaders believe that quantum computing advances will break existing security standards within a decade, only 7% have deployed quantum-safe certificates so far.

According to recent data from IBM's Cost of a Data Breach Report, 69%of organizations have no plan in place for post-quantum cryptography (PQC).

With ‘harvest now, decrypt later’ attacks a real risk for firms, what steps should CISOs be taking now to ensure they are prepared?

The quantum timeline

No one can give an exact date for when quantum computing will be able to break today’s widely-used public key cryptography, but the risk is close enough that governments, standards bodies and major technology companies are already acting.

The US National Institute of Standards and Technology (NIST) released its first three final post-quantum cryptography standards in 2024, while the UK’s National Cyber Security Centre (NCSC) has set a target for organizations to complete PQC migration by 2035.

Technology giants such as Cloudflare and Google are setting their own post-quantum security milestones for 2029.

The quantum threat is often compared to the millennium “bug” (Y2K). It has even earned its own moniker: “Y2Q”, or “Q-Day”.

But unlike Y2K, there won’t be “a single moment of failure”, says Mark Hughes, global managing partner, cybersecurity services at IBM. He says the reality could see risks materialize over multiple years. “Different cryptographic systems will fail earlier than others, and that all depends on design and key size,” he predicts.

Harvest now, decrypt later

DigiCert’s survey highlights concerns about ‘harvest now, decrypt later’ attacks, where adversaries steal information for use at a later date. Over 80% say at least some of their encrypted data is vulnerable, with around one-third reckoning more than a quarter could be at risk.

Simon Pamplin, chief technology officer at Certes, describes how attackers do not need a large-scale quantum computer today to create a future breach. “They can collect encrypted data now, store it, and wait until the capability exists to decrypt it.”

He says the quantum threat timeline is “much shorter than many organizations assume”.

“Whether a cryptographically relevant quantum computer arrives in 2029, 2035, or later, the data being harvested today may still be valuable when that moment arrives.”

Firms are often unaware of the type of data at risk. “People often think about passwords and payment details, but they're usually not the biggest concern,” says Tristan Shortland, CTO of Infinity Group. He says the most exposed data is information with a long shelf life: “Intellectual property, product designs, merger and acquisition discussions, healthcare records, legal documents and sensitive communications that retain value for years.”

This matters most in sectors with complex operational or national security requirements, including critical national infrastructure, financial services, energy and manufacturing, says Ben Packman, CSO at PQShield, which helped to create the quantum standards being enforced by NIST and the NCSC. “One unsupported system, legacy platform or unprepared supplier can create risk across a much wider technology estate.”

At the same time, quantum poses other threats beyond attackers stealing data to decrypt at a later point. “If future quantum computers are able to break the cryptography behind digital signatures and certificates, attackers could potentially make malicious software, fake updates or fraudulent communications appear legitimate,” warns Packman.

Mitigating the quantum threat

With the risks in mind, it makes sense to start preparing to secure data now to mitigate the quantum threat. This means building cryptographic agility into the systems, suppliers and upgrade cycles organizations already rely on, experts say.

“Organizations need cryptography that is agile enough to evolve as standards mature, threats change, and new implementation requirements emerge,” advises Packman.

He believes preparation starts with understanding where quantum-vulnerable public-key cryptography is used, which systems are most exposed, and which suppliers' systems depend on.

Pamplin concurs. From there, companies should “prioritize long-life and high-value data”, rather than “trying to treat everything equally”, he advises.

“80% of your data loss risk sits within 20% of your applications. That is where you should focus first.”

NIST calls for vulnerable public key algorithms to start being deprecated after 2030 and generally disallowed after 2035. Yet preparation must come sooner, says Paul Holt, group VP EMEA at DigiCert.

He warns that the process of inventorying an organization’s use of cryptography across the enterprise, creating post-quantum roadmaps for each use case, and executing the transition “can span many years”.

At the same time, he points out that beyond understanding quantum, security leaders are “struggling to convince the rest of the business that budget and resources are needed now”.

This is where crypto-agility becomes essential, according to Holt. “Weathering the transition to a quantum-safe posture demands the ability to adapt and scale digital certificate management. Meanwhile, organizations must migrate cryptography to approved post-quantum algorithms.”

Finally, he says certificates must be “immediately discoverable and manageable continuously and at scale”.

Yet when considering quantum preparedness, it’s also important not to rush. The quality of implementation will matter just as much as the standards themselves,” says Packman.

“Replacing cryptography as part of a planned upgrade is cheaper and less disruptive than retrofitting it later under tighter deadlines.”

He believes every scheduled technology refresh is “a chance to introduce quantum-safe capability, assess supplier roadmaps, validate implementations or retire systems that may become difficult to support later”.

While quantum attacks are still likely to be some years away, experts think IT and security leaders should start to view the area as part of the bigger picture. Shortland believes quantum is increasingly a governance issue.

“Boards routinely assess climate, geopolitical and supply chain risk over a 10-year horizon,” Packman adds.

“Quantum belongs in that category because it has the potential to undermine decisions being made about data today.”

Dr Martens is one of the most recognizable brands on earth, famed for its iconic footwear.

From the original ‘1460’ boot to more modern footwear, Dr Martens have been sported by punks, musicians, and an array of celebrities spanning all age ranges.

But with that global appeal comes a global operation, and engaging with customers is a significant challenge according to chief technology and AI officer, Graham Calder.

Calder tells ITPro that the brand has worked closely with Salesforce and Amazon to transform customer service capabilities for a modern, global audience.

Leveraging services such as Amazon Connect, Agentforce and Salesforce’s Service and Marketing Clouds, the brand is now delivering “agentic experiences” for customers. This push forms part of a broader transformation for the brand over the last decade, starting with a shift to direct to consumer (D2C) sales.

“Our products are timeless, but customer expectation and experience moves on,” he tells ITPro. “If you go back eight years, we were manufacturing and we were designing, but we didn’t do any direct consumer sales. We were almost entirely wholesale [and] worked through partners.”

“The previous strategy was about capitalizing on the opportunity of direct to consumer. So we have grown our direct to consumer channel to the point where it’s around two-thirds of our business.”

According to Calder, this shift highlighted failures with regard to customer care, especially as it now engages directly with consumers around the globe.

Dr Martens operates across 20 ecommerce markets globally and boasts 240 retail stores – from Tokyo to the US West Coast and “pretty much everywhere in between”, with its global headquarters housed in London.

“We’re a genuinely global operation with all the complexities and the complications that brings,” he says. “Part of our new strategy is that we have an ambition to be the world's most desired premium footwear brand.”

Tech fragmentation at Dr Martens

The company quickly learned that operating across these markets was having an impact on consumer relations, and a key factor here lay in what Calder describes as a highly fragmented technology estate.

“We had lots of regional solutions. We had no consistency of policy, no consistency of data, no consistency of journeys, experience,” he tells ITPro. “It was all kind of different. So we were a global brand, but we didn't turn up that way if you were doing customer care.”

That lack of consistency meant the company’s customer care proposition was “lagging behind”, resulting in a steady decline in customer satisfaction (CSAT) scores and Trustpilot ratings.

Embarking on an overhaul of customer support, Calder says the company began exploring “agentic experiences” for customers, tapping into the wave of powerful new agents capable of interacting with customers. It’s here that the work with Salesforce began.

Initially, Calder notes that the state of fragmentation was daunting, with customer care agents in different regions using different platforms, which in turn were disparate and detached.

“We had Zendesk in the USA, we had Salesforce in UK and Europe,” he says. “Neither was being used to its full potential. When I looked at the Salesforce usage in our contact centers, I was staggered. I mean, we were probably using about 2% of its capacity.”

“When you looked at the setup of the actual estate, it was completely fragmented,” he adds. “The telephony platform didn’t connect to the CRM, the order management systems didn’t connect to the CRM. Nothing was connected.”

Calder describes this early stage of the transformation as a “classic human swivel chair integration” that required a complete rethink of the technology stack.

“There was no consistency, which actually is quite a useful thing because at that point you realize I could try and fix this, or I could just assume that that is not where you would start and just get rid of it,” he explains.

In this case, Dr Martens chose the latter. The company pivoted to Salesforce, allowing the company to consolidate fragmented data sources and deliver a more unified approach in customer support.

“Because it was doing very little, it was also very clean, so we didn’t have to re-implement,” he says. This proved equally valuable for Amazon Connect, which plays a key role in the agentic customer experience, Calder adds.

The company has two instances of Connect “running identically” for latency purposes, and both these plug into Salesforce.

“Everything runs off the same data model, same single version of the truth, same single customer record, and it's all fully integrated into our commerce systems and all of our various other systems, which our old technology wasn't," Calder says.

“Agents don’t wear shoes” – but they do handle queries

A key focus for Dr Martens lies in the use of agents for handling customer queries. Indeed, the company set a long-term target of 75% of all customer contacts being fully automated. The company currently resolves around 50% of responses autonomously.

Applying agents was only one part of the equation, however. As with any transformation, Calder notes that getting people on board is critical.

“Whenever you’re doing digital transformation, you often get asked the question ‘what’s the most important element of it?’ and the technology people will nine times out of ten say ‘the technology?’ - but actually, it isn’t,” he says.

“It’s the people, because the technology doesn’t design, build, and operate itself. It’s the people that do that.”

Calder says that you can have the “best technology in the world” but if you don’t have the talent to use it, things will ultimately fall flat. Indeed, the nature of the “swivel chair integration” made it hard for customer care agents.

This meant the company worked extensively on boosting skills, getting them up to speed with new tools and technologies to support them in their role, and enabling them to work alongside agents.

When a human agent isn’t available, for example, AI agents will fill in the gaps, allowing for seamless engagement. Of course, there’s always a fallback in the event that customers demand a human customer care agent.

Calder adds that Salesforce’s ease of use was a key factor in easing staff into these changes.

“Part of why we selected Salesforce is it’s a fantastic platform for giving you that single pane of glass,” he says. “It’s got all of those capabilities to allow you to plug other things in. It’s all at the fingertips, it’s all integrated into the console.”

This has proved valuable so far and has had a positive impact on workers by reducing complexity. It ultimately benefits customers though, who know the brand will be on call to assist with queries – and that was always the primary focus.

“AI agents don’t wear shoes,” he says. “There’s always a human at the end of this.”

“The core of what we stand for is about the individual,” Calder adds. “It’s about self expression, creativity, your ability to be yourself. It’s so fundamental to what we do, and therefore it is really important that we always give you the way to get to the human if the automation doesn't work.”

Building on success

Dr Martens carefully considered the use of agents in customer care, according to Calder. This project aimed to improve experiences, but not at the expense of CSAT or Trustpilot scores.

The goal here was to streamline customer engagement while also boosting those numbers, which at offset were far from ideal and in the “red zone” at around 2.5 out of five on Trustpilot.

So far, the scheme has proved successful. The design work began in December last year and the project went live in June this year. Calder says the results have been eye-opening.

“Objective number one, which was [to] turn around the trend in CSAT and Trustpilot, has happened,” he says. “I think it’s been quite surprising to people that it’s happened so quickly.”

“I think we’re now back up into the amber zone,” Calder adds.

Dr Martens’ Trustpilot score currently sits at 3.2 out of five, right within the aforementioned amber zone. Over the medium term, Calder says the company wants to “get up to around 4.5 out of five.”

In September, the UK’s air traffic control system NATS suffered not one, but two outages.

In the first, a reported software glitch in NATS’ main systems caused the cancellation of over 2,000 flights. The defect happened “in the space of a millisecond”, according to an initial NATS investigation. This, in turn, caused corrupted data and forced NATS to limit UK air traffic for most of September 8th.

The second incident, a “connectivity issue” at its Prestwick centre in Scotland, was more minor, affecting some 150 flights. But at the time of writing, its full impact was still being assessed. The organization said air traffic control was back up to “full capacity” by the evening of September 21st.

Impactful beyond initial issue

Airlines, though, warned of knock-on effects. Ryanair said 140 of its flights were delayed, affecting 25,000 passengers. “Passengers are once again suffering lengthy delays because NATS has failed to deliver either a reliable system or an effective back-up,” said Neal McMahon, the airline’s chief operating officer, in a statement. Ryanair called on NATS CEO Martin Rolfe to resign, or for the government to sack him.

The failures are only the most recent to affect NATS. Back in 2023, the air traffic control service experienced another IT shutdown over the Bank Holiday weekend. Then, 700,000 passengers were delayed. A further, smaller incident forced the cancellation of 150 flights in 2025.

As far as we know, none of the incidents at NATS were caused by a cyber attack or other malicious action. Instead, it appears that all the incidents were the result of IT failures.

The organization’s initial report into the September 8th outage said that corrupted data forced NATS to restart the National Airspace System (NAS) and reload data. To do this, they had to restrict all of the UK’s airspace. The report also stated that the incident was not connected to the 2023 outage.

NATS claims it has invested more than £1bn in technology over the last 10 years. Airlines and their passengers will be questioning whether that was money well spent. But NATS's issues illustrate how fragile modern IT systems can be, and why organizations need to plan for failure.

“In its 2023 failure incident, NATS had a backup. It failed the same way as the primary, at the same moment, because it ran the same logic on the same input,” Ivan Milenkovic, vice president for risk technology EMEA at Qualys, a security vendor, told ITPro.

“After 2023, the CAA’s independent review produced 34 recommendations across NATS, the regulator, airports, airlines and government. The review was formally completed in June 2026 with all recommendations closed,” he explains. Less than three months later it failed again, in the same flight processing area. NATS says it was a different sub-system this time. Even so, I’d expect the new review to ask why “closed” recommendations didn't prevent another day like this.”

Far-reaching consequences

Clearly, air traffic control is critical to national infrastructure (CNI), and operators cannot take chances with safety. In the case of the NATS outages, the organization will have had no choice but to close airspace and to divert flights.

But disruption on this scale has consequences far beyond the inconvenience to individual passengers, or even to airlines and airport operators. They also cause a significant economic impact.

The 2023 NATS outage is calculated to have cost airlines £65m, and costs to passengers, airports, tour operators, insurers and others brought the total to £75m to £100m, according to the Civil Aviation Authority’s independent review. Understandably, governments want to see organizations, especially those running CNI, improve their resilience and recovery planning.

“It wasn’t a recent software update that caused the incident, rather a defect that had been present for some time, but the specific course of events hadn’t occurred to trigger it,” James Watts, managing director at Databarracks, told ITPro.

“You prevent this type of issue through testing software before rolling out into production. Once an incident occurs, the challenge is recovering systems, maintaining operations and minimising the impact. Fixing the defect comes later.”

As Watts says, once NATS found they had a problem, they moved quickly to implement their major incident plan. “The priority in transport and aviation must always first be to safety. NATS’ incident processes here look to be strong, and it operated according to its incident and crisis management planning,” he notes. But there are still lessons enterprises can learn.

The first is not about technology, but communications. NATS’ systems were offline for a relatively short time, but the impact was still significant. Even though NATS has come in for plenty of criticism, there are other organizations that have handled IT outages far less well. So CIOs, CISOs and heads of operations need to ensure their communications plans are as well-drilled as their business recovery processes.

More than just a tick-box exercise

Organizations also need to keep up with changing laws and regulations, even though compliance on its own does not necessarily improve resilience. Enterprises could face a growing regulatory burden while also having to redesign systems to be robust during an incident and easier to restore afterwards.

“Regulation cannot be a substitute for effective organizational and operational practices which seek to anticipate and simulate risks and take anticipatory steps to mitigate them,” James Morris, director of CSPR, a think-tank focused on business resilience. Organizations across critical infrastructure need to embed a “culture of prevention”, he argues.

In the near future, laws such as the EU’s Cyber Resilience Act, DORA and the UK’s Cyber Security and Resilience Bill will set minimum standards in some areas. But this might not be enough to make the economy better able to withstand accidental outages, let alone hostile acts.

“There is still a question over how exactly “critical national infrastructure” will be defined,” Chris Dimitriadis, chief global strategy officer at ISACA, told ITPro.

“Cyber attacks on major retailers such as M&S and Jaguar Land Rover have highlighted the wide-reaching impact of organizations that are not currently classed as critical national infrastructure. Regulation is moving in the right direction, but there is still a clear job for organizations to ensure that they are compliant.”

Businesses should review their business continuity and cyber incident plans now, and not wait for regulators to force their hands. This means reviewing skills, practices, and protocols, Dimitriadis says.

Otherwise, there could be far more than a missed flight at stake.

Experienced Managed Service Providers (MSPs) who have sold to enterprise customers for decades will be familiar with the challenge of data gravity. The larger and more disparate a global organization’s dataset was, the more difficult it was to move, access, and use, therefore raising data storage bills. Companies were unable to scale without bearing the logistical and financial weight of their data, and it was a regular pain point in new business negotiations.

However, the storage industry has evolved exponentially since then. Through the introduction and subsequent advancement of cloud object storage, cloud providers were able to alleviate much of the weight of data gravity by consolidating enormous volumes of data and eliminating the need for physical space. What’s more, compute is now able to run where the data resides inside the cloud, as opposed to needing to be downloaded for processing. Data gravity, as created by infrastructure, is no longer a primary concern.

And yet global businesses are still burdened by their data. The scale of the dataset may no longer be an obstruction, but there are new forces dragging down data management and preventing seamless access.

The weight of cloud cost

The first new “data drag” is cost – not the costs associated with the size of the dataset but the cost of moving and maintaining data. From egress fees to tiered storage penalties and rising infrastructure complexity, the cost of moving and maintaining data is preventing businesses from seamlessly using their own information on demand.

When choosing a storage solution, data management fees can play an influential role in purchase decisions. Our 2026 Cloud Storage Index revealed 48% of UK businesses’ cloud spend was spent on fees rather than storage itself. Then, nearly half of organizations are overspending on cloud and 84% point to fees as a key driver of excess spend. So, it is imperative channel partners understand the financial paradigms of each cloud player to be able to offer a bespoke fit that suits their customers’ budgets and requirements.

Take egress fees as an example of how pricing mechanisms weigh down data autonomy. They are incurred whenever data is withdrawn from a cloud provider’s infrastructure and are particularly problematic as they inhibit customers from accessing and moving their own data. As a result, companies often find themselves fully dependent on their cloud provider, locked in by their data.

The weight of AI lock-in

Fees are not the only mechanism weighing businesses down. As organizations adopt and feed their data into AI platforms such as ChatGPT, Claude and Gemini, a new dependency layer is emerging. These tools risk becoming tightly coupled with specific data environments, making interoperability between platforms increasingly difficult.

We’re seeing cloud providers bundle their tools in a way that demands customers use all their data services exclusively. For example, recent submissions to the UK’s Competition Market Authority strategic market status investigation of Microsoft reveal instances of organizations having to pay for the full suite of products despite only planning to purchase Microsoft Teams, for example. Now, AI appears to be the latest channel for vendor lock-in.

With the cloud hyperscalers entering the AI game, and their historic tendency to capture vendor lock-in by bundling commodity and proprietary services, it would not be surprising if they tied down their AI customers into complex contract mechanisms with cloud, compute, and other services. The cloud industry risks AI reinforcing these walled gardens that prevent the flexibility and autonomy businesses need to innovate and progress.

Customers may then feel pressured to commit to one provider for all their AI and data management needs, but the data shows that 72% of UK businesses prefer hybrid strategies to manage their AI workloads. It is up to their channel partners to help find a hybrid solution of data storage and management providers that allow, by default, easy data export and transfer.

Data Gravity blocks progress

While the enormous scale of enterprise datasets is no longer the ball and chain it once was, both the cost and AI lock-in of contemporary data management have stepped into its place as elements of a new data gravity.

Friction from fee structures and AI ecosystem dependencies are both restricting businesses from accessing and using their own data as they need, thus preventing them from unlocking innovation.

To support their customers, channel partners must understand the nuance of data storage and management in an AI era. Only by doing so will they be able to help guide their customers through purchase decisions to find not only the best solution for their storage needs but also the best-fit storage solutions that support and drive their overall business objectives.

Rising geopolitical instability, cyber threats, and concentration risks associated with data localization are prompting governments to move beyond traditional sovereignty models centered on data residency and geopatriation. As a result, data embassies are emerging as a practical approach to ensuring continuity of government services and operations.

These legally protected enclaves enable a nation to host critical digital infrastructure on foreign soil while retaining full sovereignty and data control, and interest is being led by small, highly digitized states facing active conflict or severe hybrid threats, according to Daniel Nieto, senior director analyst, Gartner.

“Sovereign dispersion decouples legal authority from physical location, whereas multi-region cloud back-ups simply move data geographically while remaining entirely subject to the host nation’s courts, laws and warrants,” he says.

“A true data embassy is established through formal bilateral treaties that grant foreign-hosted server racks the same statutory diplomatic immunity and inviolability as a physical embassy. Practically, it maintains an active, low-latency ‘digital twin’ with automated rerouting, keeping core civic services running during a crisis, rather than sitting as a passive backup repository.”

During a ‘black sky’ event, such as total domestic infrastructure collapse from a cyberattack, power failure, or physical strike, automated network routing immediately redirects traffic to the extraterritorial node, explains Nieto.

“This ensures citizens can continuously access core registries, tax systems and central bank ledgers even if the physical capital is entirely compromised.”

The rise of the data embassy

Gartner predicts that at least 15% of nations in unstable regions will establish formal data embassy agreements by 2029. The world's first was established back in 2015 by Estonia, born out of an analogue workaround that saw diplomats transport hard drives of critical state data to Estonian embassies abroad.

“We thought, okay, we’re very much a digital state, so why are we doing this manually? Maybe there’s a better framework for that," says Allan Allmere, IT architecture and infrastructure advisor, Estonia’s Ministry of Justice and Digital Affairs.

That question led to the creation of its data embassy as a ‘last mile’ solution to ensure E-Estonia, the country’s digital backbone, would continue to run “if all else failed.”

Nothing like it had been attempted before, so it meant finding a country willing to even define what a ‘data embassy’ was. Estonia wanted to stay within Europe and found two countries genuinely open to the idea. “We settled on Luxembourg, partly because smaller countries are typically more flexible and agile, and partly because it had Tier IV rated data centers – the highest classification available, used by NATO.”

“Tier IV certification provides the fault tolerance and continuity that critical systems require, and is the same standard provided by the Government IT Center to their public administration clients,” adds a technical advisor at Luxembourg's government IT center.

“Hosting a partner state’s critical systems builds on that same foundation, with the addition of a dedicated environment and a specific bilateral legal framework.”

That legal framework rests on firmer ground than it might appear, as when it comes to international law, the consensus is that existing international rules apply, says Agnes Kasper, head of the Law Branch at NATO's Cooperative Cyber Defence Center of Excellence (CCDCOE). “There is no 'new law,' the question is rather how this vast existing law applies.”

She notes that the Vienna Conventions on diplomatic and consular relations – largely customary international law, binding even beyond their signatories – predate the cyber age and don't directly address arrangements like data embassies, "which is why it was necessary to write specific conditions into a separate intergovernmental contract."

Continuously evolving

Allmere treats Estonia’s data embassy project as a startup: “It's never done; it's always evolving.” There have been three steps so far, starting with storage of 10 critical datasets. This has since expanded, and step two was the addition of a live government cloud in Luxembourg, which was added this year. “This gives agencies an active site abroad rather than pure backup. Step three is still in development, but will be a long-term archival layer, built for recovery decades into the future, when current technology may be obsolete.”

Ministries are responsible for their own datasets, each preparing a full backup that will enable rebuilding, “keeping in mind that that might not be them,” notes Allmere. That backup is then encrypted and sent directly to Luxembourg over a private encrypted connection.

The data can be downloaded from the embassy at any time, but as a security measure, nothing can be altered or deleted and, to ensure readiness and resilience, each ministry must undertake a mandatory restoration test each year.

Data protection relies on multiple layers of security controls. At the state level, this hinges on a principle Nieto calls encryption as a border including encryption as a border (EaaB). “This is where data is fully encrypted using hardware security modules (HSMs), with keys retained strictly within the origin state – ensuring the host nation holds the hardware ‘lock,’ but only the home nation holds the ‘key.’

“For maximum security defense and intelligence workloads, this is bolstered by post-quantum cryptography, zero-trust architectures and physical air gaps.”

Enterprise takeaways

Estonia's approach may be a national-scale answer, but the same residency-versus-resilience question is now landing on enterprise IT leaders' desks, well before most of them are forced to make a similar call with their own data.

“Enterprise IT leaders must realize that physical data localization offers a false sense of security, as concentrating infrastructure locally creates an operational target. True resilience requires decoupling data location from operational control by maintaining strict encryption key sovereignty across dispersed locations,” says Nieto.

“Furthermore, enterprises should architect systems using containerized applications managed via Infrastructure as Code (IaC), allowing them to rapidly tear down and redeploy their entire digital stack to a neutral jurisdiction within hours if geopolitical or vendor risks escalate.”

While replicating the data embassy model would be considerably more difficult for private businesses, experts agree that many of the technical resilience principles are transferable.

“Without diplomatic machinery, a business can pilot a low-cost version of sovereign dispersion by decoupling its encryption keys from its cloud hosting provider. An enterprise can store encrypted secondary database replicas in a stable, foreign cloud region while retaining total control of the decryption keys in an on-premises or neutral third-party HSM,” says Nieto.

“Combining this with IaC allows the business to test asynchronous, containerized failovers to an isolated secondary jurisdiction without handing control over to a single vendor or host state.”

The underlying logic behind a data embassy still applies to businesses’ distributed continuity solutions, adds Allmere. “You need to make sure you don’t put all your eggs in one basket – don’t trust just one location, or even just one provider.”

He goes on to highlight the risk of cloud concentration. "Two or three years ago, I was in the camp of ‘let's put everything in the cloud, and it’ll be safe.’ Now I think that’s not so wise, because 71% of the whole cloud market in Europe is owned by just three US companies.

“You also need to consider how fast you can move from one cloud to another if needed. Feel free to use the big providers like Amazon, Microsoft, or Google, but I recommend avoiding their bespoke offerings, or you could become locked in,” he concludes.

A 2025 report on the ‘State of AI’ by McKinsey looked at how organizations are “rewiring” themselves for AI and, among various useful insights, it highlights the pace at which organizations are changing.

As recently as 2023, the emphasis was on experimenting with generative AI through small pilots and employee productivity tools. Fast forward to today, and attention has turned to embedding AI in production environments, with one of the major talking points being the associated infrastructure constraints.

As far as Managed Service Providers (MSPs), Value-added Resellers (VARs), and other channel providers are concerned, customers are no longer simply asking for infrastructure capacity; they also want guidance on how best to support increasingly demanding, real-world AI workloads. Compared to conventional enterprise systems, these applications have very different infrastructure requirements, with GPU resources and cloud costs quickly becoming much more significant considerations.

What has changed?

There can be no doubt that many organizations and indeed their channel partners initially assumed that AI initiatives could be accommodated within existing cloud strategies. Looking back, that’s completely understandable, but while it may have been sufficient during the experimentation phase, production AI is a different beast.

AI projects also move at an unprecedented pace. Development teams want to experiment quickly, train models, test new capabilities, and deploy successful applications without waiting weeks or months for additional infrastructure to be provisioned. Add to that growing concerns over AI token spend as organizations scale production deployments, and infrastructure decisions must urgently balance harsh economic realities with technical capability.

Embedding AI into business-critical processes also brings new questions about data sovereignty, particularly around where information is processed and who controls the supporting infrastructure. The stakes are also higher. AI applications are increasingly handling business-critical data, meaning organizations need confidence that they retain appropriate control over their data and the infrastructure that supports it.

Collectively, these factors are encouraging organizations to reassess long-held assumptions about infrastructure. For instance, is it still the case that public cloud is the default answer for every workload or that compute resources are effectively unlimited and readily available? Is infrastructure cost relatively predictable as applications scale? The list goes on.

Staying focused

If the answer to any of these questions is no, some strategic adjustments may be required to ensure AI projects remain properly specified. Let’s be clear, this isn’t necessarily about wholesale change or yet more transformation. Take hyperscale cloud providers, for example, which remain central to enterprise IT because they enable organizations to deploy applications on demand and at scale without significant upfront infrastructure investment.

But it’s also clear that AI workloads can become expensive to run in public cloud environments, particularly where resources are required over sustained periods rather than for short-term or intermittent demand. This situation is unlikely to change soon and may become even more pronounced as agentic AI is integrated.

The answer is to look more closely at where AI workloads are running and what is driving infrastructure costs. In this context, visibility is increasingly important, particularly as environments inevitably become more complex. Organizations want guidance around whether their applications are best suited to hyperscale cloud platforms or would benefit from specialist infrastructure that is better aligned with their specific requirements. The mindset is somewhat different in that it is workload-led rather than platform-led.

Dealing with uncertainty

This is precisely the kind of scenario where channel partners have always added the most value. Customers increasingly want to understand the commercial implications of different infrastructure choices, not just their technical characteristics. They also expect clear, objective recommendations that reflect their business priorities rather than a preference for a particular platform or vendor.

So where does that leave us as channel stakeholders? One of the biggest challenges facing customers is that AI infrastructure decisions are being made against a backdrop of exceptional technological change. Assumptions that appear reasonable today may need to be revisited within months.

Let’s be honest, few organizations can be completely certain how AI adoption will evolve over the next year, let alone the next two or three. Infrastructure strategies therefore need to accommodate uncertainty rather than optimize for a single expected outcome. This explains why the industry is seeing much more made of the advantages that infrastructure flexibility offers. Customers increasingly make decisions based on how well they preserve future options rather than locking them into a particular technology or commercial model.

This changes the nature of conversations between customers and channel partners. Rather than recommending a destination architecture, partners increasingly help customers make decisions that remain resilient as AI takes its next exciting turn. Getting this right will increasingly influence positioning and competitive advantage.

The channel has fundamentally been built on change. From cloud adoption and zero trust to AI, partners have positioned themselves as trusted guides, helping customers navigate the availability of new technological innovations. Yet as technology advances at an unprecedented pace, so has the scale and complexity of cyber risk. This is being driven not only by technology itself, but by the people using it. As a result, the role of the channel partner is evolving with it.

Success for channel partners is no longer about chasing every emerging trend or expanding into every new technology category. Instead, the partners that will shine are those that become agile, specialized advisors, helping organizations navigate AI, compliance requirements, and the evolving threat landscape. A key part of that role is secure behavior management, which means strengthening the human layer of security through continuous and measurable behavior change.

However, as the pace of innovation continues to accelerate, this raises an important question: can channel partners realistically keep pace with technological change whilst simultaneously empowering customers?

The challenge of keeping pace

In recent years, the cybersecurity industry has witnessed significant growth in technological advancements, with new solutions emerging weekly, regulatory requirements tightening, and threat actors becoming more sophisticated in their tactics. On the one hand, this has greatly benefited channel partners by providing an opportunity to expand their portfolios. Every innovation presents another opportunity to sell, another vendor relationship to manage, and another service to offer. On the other hand, this approach often creates more problems than it solves.

Partners that focus solely on emerging trends frequently end up with complex technology stacks that are difficult to explain, differentiate, and sell. As a result, customer conversations become focused on the product rather than on real business outcomes.

The partners experiencing the strongest growth are taking a different approach by focusing on understanding the problems their customers are trying to solve. By developing expertise in specific areas, partners are able to build credibility around those challenges and create clear pathways to measurable outcomes. This, in turn, enables them to act as trusted advisors that customers can rely on.

From supplier to trusted advisor

Becoming a trusted advisor within the channel has never been more critical. Customers are often overwhelmed by choice, particularly in a crowded market. For this reason, they require partners who can help them understand risk, prioritize investment, and make informed decisions.

While this shift doesn't typically happen overnight, that doesn't mean it's not attainable. An example of this can be seen in an MSP partner who initially positioned behavioral security technology as a straightforward add-on to phishing simulations and compliance training, with conversations centered largely on awareness activity. Then, within a few months, they changed their approach to focus on measurable human risk.

Rather than focusing on training delivery, they began helping customers understand and measure human risk. Behavioral data became a regular part of customer reviews, enabling meaningful conversations about where risk existed, what was improving, and where additional attention was needed. As a result, the service evolved from a product sale into a managed secure behavior management program, and customer relationships became significantly stronger. Customer relationships also became significantly stronger. The solution became embedded within wider security discussions, opportunities for expansion emerged naturally, and the partner established itself as a strategic advisor rather than a supplier of tools.

This shift illustrates an important lesson for the wider channel. Customers place greater value on partners who can interpret data, provide context, and guide decision-making than those who simply provide technology.

Why human risk is becoming a channel opportunity

Managing human risk remains a persistent challenge for many organizations, and that persistence is what creates a real opportunity for forward-thinking channel partners to capitalize on. Despite years of investment in security awareness training, 62% of confirmed breaches still involved the human element, according to Verizon's 2026 Data Breach Investigations Report . This demonstrates that traditional security awareness training methods, including e-learning modules and annual refreshers, remain ineffective, and it's time for organizations to take a different approach.

The focus should move away from training completion rates and compliance tick-boxes towards establishing baselines for risky behavior, demonstrating clear reductions over time, and providing evidence of genuine improvement, reflecting the core principles of secure behavior management. The partners who make that shift now will not only differentiate themselves in a crowded market but will position themselves as the trusted advisors that forward-thinking organizations increasingly need.

Looking ahead

As the cybersecurity solutions market continues to develop, the most successful channel partners will be those that recognize technology alone is no longer enough.

Organizations require guidance on how their employees interact with that technology, where behavioral risks exist, and how those risks can be reduced over time.

Secure behavior management provides partners with an opportunity to move beyond transactional product sales and deliver ongoing, measurable value. In doing so, they can deepen customer relationships, create recurring service opportunities, and strengthen their position as trusted advisors at a time when organizations are looking for greater clarity in an evolving security landscape.