Israeli digital advertising agency Discover-Tech has been caught serving millions of online adverts that installed spyware on the phones of targets around the world.

Through a zero-click technique dubbed "AdFection", Discover-Tech's Controlium platform has been used by financially motivated customers to de-anonymize, fingerprint, and automatically infect devices the moment an advertisement is rendered.

Apps found displaying Discover-Tech ads include Duolingo, dating apps, period trackers, German weather apps, Yahoo Mail, Viber, and calling apps such as Truecaller.

This is the first example, said Citizen Lab, of in-the-wild infection of devices with zero-click mercenary spyware via targeted advertisements.

The spyware, said Citizen Labs, was used against dozens of devices belonging to journalists who reported on sensitive subjects at Qatari news network Al Jazeera. Its ads also appeared in critical networks used by the Pentagon, the United Nations International Atomic Energy Agency and the International Criminal Court.

And it's also been used to hit media organizations in Austria, Denmark, France, Mexico, Israel, Italy, Norway, and Sweden, as well as networks of international media services.

"Discover-Tech presents itself as a legitimate online advertising business. In fact, the Israel-based company provides a bridge between multiple mercenary spyware companies engaged in hacking and the legitimate online advertising ecosystem," the researchers said.

"We have found billions of instances of Discover-Tech bidding to send such advertisements to users, and no sign that the company is engaged in any legitimate advertising business activities."

The firm's Controlium platform, the researchers said, allows mercenary spyware firms to infect targets via online ads. After mass-profiling and deanonymizing millions of users, they can then covertly hack individual targets by sending them seemingly harmless ads containing zero-click exploits to install spyware on their devices.

The device is sent malicious JavaScript code, which is executed by the browser or app and then exploits and infects the device.

Controlium's users include mercenary spyware company Candiru – reportedly now owned by US-based Integrity Labs. This firm has already been called out by the US Commerce Department for developing spyware used to target government officials, journalists, businesspeople, activists, academics, and embassy workers.

The platform's also linked to Israel-based Bindecy, which specializes in zero-click mobile exploits, and another mercenary surveillance firm called Intelos.

"Our analysis reveals a dangerous lack of due diligence in the advertising ecosystem that Discover-Tech actively exploited. Despite supporting mass-profiling and mercenary spyware hacking, Discover-Tech has nonetheless appeared on several lists of "certified" and "approved" advertising vendors maintained by Google and IAB Europe, the advertising industry association," the researchers said.

"The fact that a "certified" company could target millions of users for years with no signs of legitimate activity highlights the dangerously inadequate controls over a sector that by its very nature touches almost every individual with a mobile device or a web browser in extremely invasive ways."

Google and digital marketing and advertising platform IAB Europe have now removed Discover-Tech from their ad vendor lists.

Does the Samsung Galaxy Z Fold 8 work as a business phone? The South Korean electronics giant clearly believes so because it has released an Enterprise Edition of its revitalized foldable smartphone.

To be clear, there's nothing new on the device, which launched midway through the summer. There isn't a specific capability or feature that's been added for enterprise workloads. It's more about the way you can purchase it and what it has to offer thereafter.

You get a Knox Suite enterprise plan, which is a single platform to manage your devices, from deployment through to individual device security. You get a guarantee of seven years of updates. And you also get Samsung's Quickstart service, which handles the setup of all your devices, including the Knox Suite.

It's also very expensive, regardless of whether you buy it as a consumer or a business. It's £1,709 in the UK, and $1,900 in the US – though that's the consumer option, as the Enterprise Editions are not available across the pond.

Samsung Galaxy Z Fold 8: Design

At launch, the audience let out an audible gasp when the Z Fold 8 was first revealed, because it isn't your standard foldable. It isn't long and tall; it's short and square, almost like a mini passport when folded. And it's thinner than ever. Unfolded, it's like a sheet at just 4.5mm. And oh so light at just 201g. This makes it both pretty and delicate.

And that slight change in form factor gives you what you really need from a foldable display: an optimal aspect ratio for reels and social content. By default, the Z Fold 8 is set to 4:3 – or 3:4 for portrait – and that is even the case when it's folded. Which, as I have quickly learned from my time with the device, is perfect for reels and scrolling socials. The 120Hz refresh rate also helps here.

That being said, however, one thing that became quite apparent early on is that I didn't really like to open the screen up. Part of that was the fear that someone might call me and force me to answer with a tablet on the side of my face, which looks ridiculous. But the main reason is that I simply don't need the bigger screen when I am out and about.

In fact, I really like the mini display. I found its passport-shaped form very handy; I took my photos with it, I recorded video with it, and I generally accessed all my apps without opening it fully and had no issues. Essentially, this is the form factor for me: a shorter, squarer device.

Another issue, maybe too specific to me, is finding the orientation when you pull it out of your pocket. Often when I did use the main display, I would either have it upside down, so the cameras were at the bottom, or I would lose the position of the volume controls – I had a similar issue with the Motorola Razr – which is probably the biggest reason I wouldn't have a folding phone.

Samsung Galaxy Z Fold 8: Displays

Both the main and external displays on the Z Fold 8 are Dynamic AMOLED's with a 1972 x 1248 resolution, and they both also have a 120Hz refresh rate. The smaller display is 5.5in while the main one is 7.6in.

For our colorimeter tests, we focused on the main display, with the 'Standard' settings. Its brightness peaked at 374.55cd/m2 and it scored 98.4% for sRGB color coverage and 101.6% for volume. The scores are comparable to the S26 Ultra, or the Honor Magic V6 if you want a foldable comparison.

The only negative was a low Adobe score (69.7%), which pretty much rules out editing photos to a professional level. The more mobile editing software we have, the more we really need mobile devices with higher scores here.

As mentioned, I have a preference for the outer display, particularly when out and about. I attended Logi Work 2026 earlier in the week, and the majority of my images and work messaging were done with the 5.5in display. Perhaps more seasoned foldable users (someone who has it as a daily driver) would use the main display more, but for me, the short display is king.

The only other issue, certainly from a business perspective, is the lack of the Privacy Display on either screen. For now, it appears to be just for the S26 Ultra.

Samsung Galaxy Z Fold 8: Specs and Performance

Inside the Z Fold 8 is a 3nm developed Qualcomm Snapdragon 8 Elite Gen 5 processor, which is paired with 12GB of RAM and 265GB of storage. The 8 Elite Gen 5 is a snappy chip, performing much better than its predecessor in our tests, making our experience of the Fold 8 very smooth.

In Geekbench, it notched 3,117 for single-core workloads and 10,716 for multi-threaded. These were far better than the previous Fold, though both were slightly under the Honor Magic V6, which we tested at the start of the year.

Samsung is very pushy when it comes to on-device AI, and has made lots of tools available, particularly for camera and video enhancement. But there's also been more use of contextual AI, with the 'Now Nudge' feature. It took me a while to get going with it, you need to turn it on in the AI settings and agree to let it use your data. And you also need the right apps and use cases for it, but essentially it reads your screen and offers tips and shortcuts. So for example, if a date or location is mentioned in an email, it will pop up on the screen with the calendar info. It's quite good at showing conflicting meeting times, in my experience.

You might see the 4,800mAh battery as a compromise, considering the Ultra model has a 500mAh battery, but it doesn't actually equate to better battery performance. The Fold 8 lasted just under 25 hours in our looped video test, which is very impressive for a foldable phone.

Even more impressive, running the same test on the cover display produced even better results – over 30 hours. By the end of this review, I may have a solid argument for using the cover display entirely.

Samsung Galaxy Z Fold 8: Cameras

You get four cameras on the Z Fold 8: two 50MP lenses on the rear – these are the main cameras – and two 10MP cameras housed in the two displays. Essentially, it's two selfie and two rear cameras. The rear cameras are wide and ultrawide, and the cover camera is also ultrawide. However, none of them are telephoto lenses, but that's arguably why it's so thin and light.

I captured a fair amount of decent images on the main cameras. The lackluster zoom made it hard to get enough detail from subjects at a distance, such as someone on stage. And I also noticed lots of visual noise around the edges. But it captures color that's true to life, and the two selfie cams produce nice warm images.

Due to the shape, you can get a little lost in aspect ratios; I certainly did, going by how many of my images ended up being square. But the camera interface does a great job of simplifying all of its options and controls.

One issue I have with the cameras is actually the folding aspect: unlike previous models, or the Z Fold 8 Ultra, I couldn't get the display to stay fixed in a folded position. So you can't really capture yourself with the timer or a video hands-free. I managed it a few times, but it wasn't very long before the device snapped open.

Samsung Galaxy Z Fold 8: Is it worth it?

As a foldable device, the Galaxy Z Fold 8 is truly game-changing. And that's just the shape of it. It's the 'reel deal', if you excuse the terrible pun. Both displays are buttery smooth, colorful, and well optimized for battery usage.

However, there are a few nagging issues, such as the lack of a telephoto lens, and there isn't really enough on the device to make it a proper business phone. The Enterprise Edition extras at purchase are great value, but we would have liked more from its on-device capabilities, like Now Nudge. That being said, it's the most interesting foldable device there has ever been.

Samsung Galaxy Z Fold 8 specifications

The Metropolitan Police Service (‘the Met’) has landed itself in hot water with the UK’s data regulator, the ICO, after members of staff uploaded sensitive data to an unauthorized third-party platform.

Between March 2023 and September 2024, officers and staff in the Greater London police force used the Auror intelligence-sharing platform as part of a crackdown on shoplifting, despite it not being formally approved for use.

Auror is a third-party, cloud-based platform from SaaS provider Auror Europe Limited, a wholly-owned subsidiary of New Zealand firm Auror Limited.

It's accessible via a website and mobile application and is mainly used by retailers to record, manage and analyse incidents of retail crime. It does this using a range of technologies, including facial recognition, automatic number plate recognition (ANPR) and AI.

Retailers can upload incident reports, images and CCTV footage relating to suspected shoplifting and other retail-related offenses, which can then be shared for free with law enforcement authorities.

This may seem not only harmless but actively helpful in the detection and prosecution of crime, however the tool wasn’t subjected to the usual data protection assessments or governance before use.

"Police forces hold large volumes of sensitive personal information and must have robust safeguards in place to protect it," said David Doodson, ICO head of investigations.

"Whilst new technologies can deliver significant operational benefits, organizations cannot allow innovation to outpace their data protection responsibilities. Before introducing new systems or platforms, they must properly assess the risks, put appropriate safeguards in place and ensure effective oversight."

Specifically, said the ICO, the Met unlawfully processed personal information without individuals’ consent and in situations where sharing wasn't actually necessary for law enforcement purposes.

In all, around 500 accounts were created, with 375 actively used for policing activities. The ICO found that, between January and September 2024, personal information relating to 179 people – three of whom were under the age of 18 – was uploaded to the platform as part of efforts to identify individuals suspected of shoplifting.

This included names, dates of birth and, in a small number of cases, home addresses.

The police force also failed to put the appropriate technical and organizational measures in place to make sure that personal information was protected or that assessment and approval processes for third-party technologies were followed.

Additionally, the ICO found that, not only did the Met not carry out a data protection impact assessment, it still isn't ensuring that only approved third-party technologies are used to process personal information.

Since the issue first emerged, MPS has tightened up its procedures. However, the ICO said it still needs to do more.

It has ordered the force to strengthen its approach to identifying, assessing and approving any third-party services used to process personal information. It must also review its controls, improve governance processes, enhance staff awareness and regularly test the effectiveness of the measures it has in place.

Controlling the spread of shadow AI

Shadow IT – and shadow AI in particular – is a growing problem. A survey from SailPoint earlier this year found that two-thirds of organizations can’t account for the information staff are sharing with AI platforms and large language models (LLMs). It also found that 35% of staff are sharing data through external tools, rather than approved internal applications.

"This action should send a message across the policing sector, and beyond: if you are using new technology to process people's information you must do so lawfully, transparently and with appropriate governance from the outset," said Doodson.

"Where organizations fail to protect people's information, we will take action. Our role is not only to hold organisations to account, but to drive improvements and promote the standards people have a right to expect."

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

Dell, HPE, NetApp, Oracle, and CoreWeave all saw their shares stumble this week, amid concerns that the AI infrastructure buildout is poorly timed and the significant investment may not pay off.

The selloff follows concerns that data centre and cloud companies may be overvalued and that enterprise spending on AI isn't enough to cover the costs of the trillion-dollar infrastructure buildout. At the same time, Gartner warned that enterprise and consumer demand is failing to prop up that spending.

Dell, HPE and NetApp’s shares all declined yesterday — 11%, 8% and 7%, respectively — following a damaging report from analysts at Barclays, which downgraded the companies ratings saying they "don't see a meaningful AI inflection yet," and raised concerns about "enterprise AI timing."

Though the decline caught attention, all three firms have seen their shares climb dramatically over the course of the last year, with Dell's share price up more than 300%.

Still, the share slide spread to others in the industry, with Oracle down 9% and CoreWeave slipping 15%.

The Barclays analysts suggested that businesses are spending just 5% of IT budgets on AI, adding that the strong profits seen at data centre-adjacent companies may reflect high prices caused by component shortages rather than an industry shift to AI.

"There is growing sentiment about enterprise AI adoption creating a hardware refresh," the analysts reportedly said. "We believe the strong enterprise revenues are more of a function of memory costs than a move to enterprise AI at this time."

Not enough AI demand?

Major hyperscalers and AI leaders are expected to spend more than a trillion dollars rolling out AI infrastructure such as data centres this year alone, via a network of deals that sees cloud providers, hardware makers and AI developers investing in each other.

AI developers continue to struggle to pay for the high costs, however, with reports that OpenAI's annualized revenue is on track to be $50bn — up significantly from the year before but $20bn behind previous estimates. Anthropic's annualized run rate exceeded $65bn over the summer, and it's set to top $100bn by the end of 2026, according to Reuters reporting.

"In 2026, AI providers are expected to spend nearly $1 trillion on AI infrastructure, and are forecast to invest an additional $6 trillion between 2027 and 2030," said Will Sommer, senior director analyst at Gartner. "However, our research indicates that enterprise and consumer AI revenue will be insufficient to support profitable returns on all planned AI capital expenditures during this period."

Earning its keep

Previous research by Bain & Company suggested that the AI industry is going to need to be worth $6trn within the next few years — with $4trn in entirely new applications and services — in order to pay for the ongoing investment.

Gartner's latest analysis suggested AI data centre owners will need to generate $8.5 trillion in revenue to cover already announced capital spending, financing costs and a 12% return on invested capital. The analyst firm thinks this won’t happen until 2032 at the earliest, leading to trillions of dollars in write-downs.

"However, total enterprise and consumer AI revenue that can be attributed to AI data centers during this period is forecast to reach no more than $3.6 trillion," said Sommer. “Because most AI-native providers operate asset-light business models, we predict that less than half of AI revenue will be earned by providers that own significant AI infrastructure."

Neocloud providers — those who build specifically for AI workloads — could be the most at risk, Gartner added, as hyperscalers have more diversified revenue sources to keep them afloat. The analyst predicted that 80% of neocloud providers will "cease independent operations" by 2030 because of such pressures.

Releasing AI-enabled services is one thing – what’s crucial is that your customers find value in your products. During the Workday Community Keynote at the tech firm’s Rising event in Las Vegas, two business leaders explained how their organizations are making the most of AI.

Rob Enslin, chief commercial officer at Workday, opened the keynote by suggesting that Workday’s product announcements this week have been focused on bold AI-enabled innovations with customers at the center. “It’s all about how you move from asking AI questions to getting the real work done,” he said.

To highlight Workday’s customer-centric approach, Enslin welcomed two business leaders to the stage. First up was Mike Harmer, VP of enterprise business platforms at Intermountain Health, who said his healthcare organization must deliver high-quality care cost-effectively, with technology playing a crucial role.

As a long-time Workday customer, the organization wanted its data consolidated in one place to exploit AI. Harmer also wanted to move beyond a system of record and create a system of action, where professionals could use data-powered services to make it easier for caregivers and administrators to boost their efficiency and spend more time with patients.

With solid Workday foundations in place, Harmer’s team explored AI. Intermountain Health leaned into Workday’s early adopter programs to test agents in a sandbox and build confidence. So far, they’ve used Developer Agent, Payroll Agent, and Self-Service Agent to develop use cases and solve business challenges.

Harmer is excited about the promise of Workday’s AI and data technologies: “When these tools get into the hands of our caregivers, it's exciting to see what they do with them. I think they're going to very intentionally disrupt and invent new ways to do things that weren't possible before.”

Sarah Clements, head of HR and shared services at BMO, explained to the audience how the bank launched Workday’s self-service agent to all 55,000 employees in June. Her digital-first organization is eager to embrace AI while simultaneously managing risk.

To move forward with agentic technology, Clements and her team engaged stakeholders across the business and created a responsible AI Forum: “We spent time with them explaining what the agent would do, and actually, more importantly, what it wouldn't do.”

The HR team looked at the bank’s policies and procedures and brought everything into the Workday platform. With the foundations in place, they began to explore use cases for agents, checking that technology answered questions in the way people anticipated.

“The number one thing we're trying to do is make things easy for employees and managers so that they can do their jobs,” Clements said, referring to the implementation of Workday’s Self-Service Agent and the use of natural language to ask key questions about key business activities, such as producing a mortgage letter for a customer.

European businesses are over-reliant on American AI providers and their laissez-faire attitude risks putting the continent at the mercy of closed systems and unexpected access restrictions.

That’s according to Mistral AI co-founder Arthur Mensch, who penned a column in Le Monde arguing Europe will be subject to an “an economic regime of dependence without equivalent in the history of civilization”.

Mensch claimed that “serious people” in Europe have suggested it’s better for the US and China to spend the millions or billions of Euros needed to create frontier AI models, rather than strain the European economy.

“According to them, it’s better to follow the recipe of the internet, where the value is in creating new applications and focus our efforts on what Europe knows how to do: its industrial data, its SMBs, and professional expertise. For everything else, open models created by others will suffice,” said Mensch.

“This is a fundamental error, which will cost Europe its place in the world,” he added. “AI isn’t the internet. The internet relied on open protocols that nobody owned. Closed AI models are proprietary. What an application or agent knows to do is decided by the model.”

Mensch pointed out not only can the owners of these proprietary models hike prices or retire a version of the model without user recourse, they can also cut access without notice. This isn’t hypothetical, as evidenced by Anthropic cutting off access to Fable and Mythos in June 2026.

“Access was reestablished, but the decision wasn’t taken in Europe, nor for Europeans. Nobody asked our opinion,” he added.

Mensch’s column comes amid heightened tensions over AI dependence and a rising interest in sovereign cloud and sovereign AI.

In the UK, the government established a £500 million startup fund to boost the country’s sovereign AI ambitions. Businesses in the country have also started to turn their attention towards sovereign AI in order to reduce their dependence on overseas providers.

Nevertheless, sovereign AI can have hidden costs. According to Forrester senior analyst Dario Maisto, this can mean slower roll out due to a skills deficit and a misunderstanding of what ‘sovereign’ is when it comes to infrastructure.

There’s also a monetary cost: research commissioned by CCIA Europe has claimed the EU sovereign AI bill could cost six times more than the bloc’s own upper estimate of €18 billion. Instead, the trade body – which is made up of non-EU businesses, primarily from the US – has predicted a potential cost of €115 billion over 10 years.

According to Mensch, however, the price is worth paying.

“If Europe resigns itself [to the status quo], AI will continue to be created elsewhere and we will only have access to second tier export versions, in exchange for licenses that will cost trillions of Euros,” he said.

Two critical flaws in one of the most widely-used LLM gateways, LiteLLM, could let low-privilege users execute code, hijack admin accounts, and steal AI provider API keys.

With 97 million downloads per month and more than 59,000 GitHub stars, LiteLLM runs inside large enterprises, startups and many of the platforms used to build AI applications.

It functions as an LLM gateway, used for storing API keys for every AI provider the organization uses, managing user access through roles and permissions, routing prompts and responses between applications and providers and tracking usage and costs across models.

However, according to Checkpoint researchers, CVE-2026-35029 allowed any user – including those with read only access – to reach administrative functions and take over the gateway. CVE-2026-59823, meanwhile, allowed attackers to redirect gateway requests to their own server and capture AI provider API keys.

"In practical terms, anyone holding an account on a vulnerable LiteLLM deployment could take over the gateway and use it for malicious purposes," said the firm.

LiteLLM uses role-based access control, where administrators manage the system and other users receive limited or read-only access. However, the CVE-2026-35029 access control vulnerability means that users with minimal privileges could reach administrative functions controlling the gateway’s core configuration.

From there, an attacker could read files stored on the server, including configuration files and system files containing sensitive information. They could also overwrite administrator credentials with their own, locking legitimate administrators out of the system, and instruct the gateway to download and execute code from an external source, enabling malware installation, data exfiltration or movement to other systems on the network.

CVE-2026-59823 is a server side request forgery vulnerability that allowed any gateway user to direct LiteLLM to send API requests to a server under the attacker’s control without validation.

Attackers could steal AI provider API keys without any compromise of OpenAI, Anthropic or other providers, use the organization’s AI provider accounts without authorization and cause unexpected costs to be billed to the organization.

LiteLLM has now resolved CVE-2026-35029 and CVE-2026-59823 in version 1.83.9, with all later versions including the fixes.

It's notable that the two vulnerabilities are nothing new: they include missing authorization checks, path traversal, RCE via dynamic module loading, and insecure password storage, and all have established mitigation patterns.

"Both vulnerabilities belong to well understood classes of security error, which shows that AI infrastructure is now exposed to the same risks that enterprise software has faced for years," warned Checkpoint.

"As tools like LiteLLM gain adoption at speed, attackers are paying close attention to them, and organizations need to secure their AI gateways with the same rigor they apply to any other business critical system."

AI gateways are starting to emerge as high-value targets, holding credentials, managing trust relationships, and controlling access to multiple AI services, said Li Zhao, principal strategic services consultant at Black Duck.

"Because many organisations consume these technologies indirectly through platforms, frameworks, and internal tooling, the risk extends beyond a single product and becomes a broader ecosystem concern," she said.

"Organizations should treat AI gateways with the same level of scrutiny they apply to identity platforms, API gateways, secrets-management systems, and other high-trust enterprise infrastructure."

The European Centre for International Political Economy (ECIPE) think tank has issued a report claiming that the EU's proposed sovereignty rules could cost the bloc billions.

The research, funded by CCIA Europe – a trade body with members including Amazon, Google, and Meta – examined the costs associated with the European Commission’s proposed Cloud and AI Development Act (CADA), which excludes non-EU providers from parts of the cloud and AI market.

The research found, the Act risks imposing €115 billion in costs over the next decade – six times the Commission’s upper estimate of €18 billion.

The reasons, said the researchers, come down to three critical blind spots in the Commission’s impact assessment:

• Failure to include €33.3 billion in direct public budget exposure
• Ignoring the cost of software re-architecting and code-rewriting, which ECIPE said accounts for 69% of CADA’s real-world impact
• Failure to take into account the cost of private-sector spill-overs, which, the report found, could amount to as much as €80 billion.

"Member states can’t rely on the Commission’s incomplete estimates when deciding CADA’s strict sovereignty requirements for cloud and AI providers," said CCIA Europe’s policy manager, Mitchell Rutledge. "Taxpayers and businesses will foot the bill, yet the official assessment missed 70% of CADA’s true impact.”

CCIA Europe is particularly bothered by Article 31 of the act, which allows the Commission to extend strict sovereignty restrictions beyond public procurement to certain private sectors, including banking, energy, healthcare, and transport.

This, it said, would increase the cost enormously, as European software vendors and digital SMEs would be forced to redesign applications currently built on global cloud architectures and AI platforms.

“Governments must recognise that CADA’s massive spillover costs for essential private-sector entities are not hypothetical but inevitable," said Rutledge. "Businesses cannot simply switch servers; they must redesign software and rewrite code across entire supply chains.”

Not entirely unexpectedly, CCIA Europe is calling on EU member states to drastically overhaul CADA, limiting sovereignty restrictions to sensitive workloads where specific public-order risks are demonstrated.

Describing corporate-origin and personnel-nationality requirements as 'discriminatory', it wants to see these rules replaced with risk-based technical safeguards.

Meanwhile, 'trusted international partners' with bilateral EU procurement commitments – like its members – should be exempted from Article 18 country screening.

“These sweeping restrictions risk draining European innovation and penalizing highly digitized member states," said Rutledge. "EU decision-makers must refocus CADA on objective, risk-based security rather than discriminatory rules that fragment the single market.”

Big US tech firms have put a lot of effort into convincing Europe that they too can provide sovereign European services. AWS's European Sovereign Cloud, for example, sees enterprise data hosted and stored solely in the region, and kept separate from other AWS Regions worldwide. Microsoft's Sovereign Cloud and Google's sovereign solutions make similar promises.

However, there are still concerns: US tech firms are subject to the US Cloud Act, which allows US law enforcement to demand access to the data they hold, even when it's stored outside the US.

Thanks to these concerns, according to Gartner research earlier this year, a fifth of European workloads are shifting to local clouds, with sovereign cloud infrastructure spending in the region set to triple between 2025 and 2027.

How many security professionals does it take to clean up the flaws found by AI? About a third more than you've got now.

That's how many more security professionals and other third-party consultants Bank of America has temporarily hired in order to work through the patches and other fixes necessary to address vulnerabilities spotted by Anthropic's Mythos and other AI security models. In addition to 3,000 existing staff, the bank added 1,000 external contractors to address the challenge.

Anthropic unveiled its Claude Mythos security model in April, but initially only gave access to approved partners via Project Glasswing. Mythos was then temporarily banned for export by the US government, alongside its Fable model, lifting the restrictions three weeks later.

In an interview with CNBC, the bank's CEO Brian Moynihan said the company had spent hundreds of millions of dollars over the last quarter fixing software as well as sorting out anti-money laundering compliance following a near-record fine from US regulators.

Finding flaws

Banks were included in the list of organisations given access to Mythos, with JPMorgan Chase an early partner, though reports from earlier this year suggest Bank of America has been testing the model since April. The main concern is the ability of the model to spot flaws in systems, sparking a wave of patching to avoid security risks.

"That risk is real risk, and that's why when the frontier-type models came out... A lot of us sat down and said, 'How do we figure out how to get through this quickly?'" Moynihan said in an interview with the American news network.

"We put on 1,000 contractors to help us do the patching work in the second quarter, and those people remain here today, helping us, on top of the 3,000 people we have in that area," he added.

However, Moynihan admitted concerns around the fact that companies rolling out AI products spotting these flaws were also offering services to mitigate them.

"We've got to stay ahead of them, and we've got to be mindful that the people doing the software development are the people trying to sell us the services," Moynihan said.

High costs

While the security work comes at a high cost, Moynihan said the company expects that to reduce over time. "Once you patch, you don't have to patch as much. ... The pipeline moves through," he added in the interview.

Bank of America predicts it will save $300m this year thanks to internal AI projects, rising to $700m by next year – however, Moynihan also suggested the bank was spending $4bn a year on new technologies, including AI.

Moynihan said on the call with analysts that "AI has helped us", with 130 projects implemented. Though he admitted "none of them are huge contributors," Moynihan predicted a 10% boost in efficiency at the same cost.

Bank of America's share price slid 8% after its quarterly results revealed a 17% slip in third-quarter profit – and after $1.2bn in fines for violating anti-money laundering rules was announced Tuesday.

Data integration and analytics specialist Qlik has achieved the AWS Data Competency in the cloud giant’s relational database category.

The AWS Competency program aims to help customers identify and grow their business with AWS partners that can offer deep industry experience and expertise across industries, use cases, and workloads.

The relational database credential now recognizes Qlik’s ability to deliver unified data architectures on the AWS platform and help organizations integrate, govern and activate data across complex environments.

The vendor said the designation differentiates it as a member of the AWS Partner Network that is capable of providing deep technical proficiency and customer success delivering data solutions on the cloud platform.

Relational database expertise

Qlik’s open, platform-agnostic model is designed to help organizations work flexibly with data, cloud platforms, and technologies without being restricted to a single technology stack. The firm’s wider capabilities include integration, transformation, governance, analytics, and AI.

Within the relational database category, Qlik provides relational database engines, migration and performance tools, as well as business applications built on Amazon Relational Database Service (RDS) and AWS Aurora.

Support for AWS-hosted databases spans Oracle, SQL Server, MySQL, PostgreSQL, and MariaDB, alongside SAP HANA, Sysbase ASE, and IBM Db2.

Benefits for Qlik customers

Qlik said its AWS recognition means customers can connect and work with data across a diverse range of enterprise environments – such as AWS, cloud platforms, on-premises systems, and SaaS applications.

They can also build on their existing environments while being able to adopt new architectures and technologies, as well as create a stronger foundation for analytics and AI.

Matt Hayes, general manager of Qlik’s data business unit, said data strategies now require greater flexibility and cannot be built around a single platform or a single destination.

“Our customers need the freedom to work across the technologies they already depend on while modernizing their data foundations for analytics and AI,” he explained. “Achieving the AWS Data Competency reflects the depth of our data capabilities and our commitment to giving organizations a more open, flexible path from complex data environments to trusted business outcomes.”

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.

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?

In-office meeting rooms leave a lot to be desired in terms of experience. Improving that won’t just make life better for your employees, one company has claimed, it may even directly lead to positive business outcomes.

That was the main message from Logi Work 2026 in London, where VP and GM Henry Levak talked more about its own solution to the problem, the recently released Rally Bar 2.

As most office workers will tell you, the spaces we use for hybrid meetings and video conferencing are complex environments. While there will be some organizations that have made bad decisions, in most cases it isn’t really the fault of IT decision-makers or the business as a whole – it’s the sheer complexity and depth of choice that causes the issue.

Much like a home, a meeting room will be a collection of products bought over time. Unless everything was shrewdly purchased from one vendor in one go and the room specifically designed for the purpose of holding hybrid meetings, it’s likely there’s a mixture of different vendor hardware and software all cobbled together. Some of it works fine but parts of it need upgrading and there’s always a difference in experience between those in the office and those at home.

This can affect office morale and productivity, according to Logitech’s own research: while 89% of employees believe a good workplace experience directly affects business outcomes, only 27% feel they have the necessary setup in the office.

“It’s a broad view of the overall IT landscape, not just specific to video,” Levak told ITPro after his keynote. “But I think most people would agree that when the office works well – that’s everything from the social elements to collaboration, the whole spectrum of collaboration – you get better outcomes from your team.”

When any of those in-office essentials break, that creates a problem, according to Levak – and these aren’t the only problems he believes need solving.

“There’s still more to do around space reservation and space management,” Levak explained. “Wayfinding, I feel like it still hasn’t been completely solved in a lot of offices. So I think from a broad spectrum of tools, IT managers and AV leaders would say there are still areas and pockets where there is still an opportunity for innovations… in some ways, that’s what we’re doing with Rally Bar 2; that’s what our announcement was saying. A lot of what people love about Logitech and meeting room experiences in small, medium, and large rooms are coming to much larger rooms. So hopefully we can bring that number (27%) up a little bit. So that people feel that outcomes are high and they have more solutions and spaces that they really want.”

The simplification of Rally Bar 2

Announced a week before Logi Work, the Rally Bar 2 and the Meeting Room System are Logitech’s answer to overly complex video conferencing setups. The second generation of the Rally Bar is a simplification itself, incorporating the original Rally Bar and the Rally Mini into one piece of kit.

“We looked at both and thought about the feedback. What is the feedback we’ve gotten over the years? What is it that people love? And simplicity is also about fewer options,” Levak said. “As long as you can still satisfy different use cases. Having a long menu of options and things to consider is complex. So we always try to distill things down to their core essentials, and what we find is that the technology today is actually quite capable of doing both things really well in one device.”

The original Rally Bar is a good example of this. Logitech used an optical camera system as it was keen to prove that Android devices could exist in much larger rooms than they were originally designed for.

Levak argued that they had achieved that pretty well, however they also wanted a wide field of view for certain rooms that just needed the different angles. Technically, it was unrealistic to do an optical camera without the wide view they wanted, Lovack admitted, as the camera solution would be quite large.

With new technology, like bigger sensors, better ISP, and faster processing, though, Logitech was able to do wide camera angles and still get that nice in-room zoom.

“Now the technology has sort of caught up; it allows us to simplify even the product catalogue, so instead of having two options, you have one option,” Levak said. “And that one option is even better than the two because it satisfies both use cases.”

The Logitech Rally Bar 2 is available now with prices starting at £3,899.

AI agents are going rogue on a massive scale, with half of enterprises reporting that they've had to deal with at least one taking unauthorized actions over the last year.

In a report based on a survey of more than 380 security and technology professionals, along with production telemetry from hundreds of customer environments, Salt Labs claimed agentic AI has moved into daily operations too quickly for visibility, governance, and containment controls.

Half of survey respondents (49.8%) said they had confirmed or suspected that an AI agent in their organization took an action in the past 12 months that wasn't intended, expected, or authorized.

Of these, more than half reported an unauthorized external API call or transaction, while more than four-in-ten reported that an agent modified or deleted data.

"The data shows a gap that is already producing real operational consequences," said Roey Eliyahu, CEO of Salt Security.

"Organizations are deploying agents and granting them access to enterprise data, APIs, and services before they can trace what those agents are doing or stop them automatically when something goes wrong. Closing that gap is the discipline of agentic security, and it is what lets enterprises give AI agents the autonomy they need without inheriting unmanaged risk."

Enthusiastic deployment meets lack of control

The vast majority of organizations surveyed (86%) are using at least one AI agent, the survey found while 60% run more than ten. Another 77% of respondents said their agents chain actions across other agents, tools, and APIs, in sequences no single team designed in advance.

Only 12.5% said they can consistently trace a complete agent path across all agents, from initial instruction through tools and APIs to outcome. More than six-in-ten believe they would need hours or days of manual work to reconstruct what a specific agent did the previous day.

When problems do occur, they're hard to deal with. Only 17.5% can automatically stop an agent that begins behaving unexpectedly in production. Most can intervene only manually, or by taking the application offline.

More than eight-in-ten said AI-generated or AI-assisted code contributes to at least some of their application security findings. Only 7% resolve issues inside the coding assistant at generation time, rather than through code review, testing, or after deployment.

Meanwhile, shadow model context protocol (MCP) infrastructure is already a live operational problem. 33% of survey respondents said they had found an unauthorized, shadow, or misconfigured MCP server in the past 12 months. 54% said they'd confirmed or suspected one.

Salt Labs' own data shows MCP-related infrastructure in 22% of customer environments in August this year, up from 16.7% in January.

Earlier this year, a survey commissioned by Aembit found that 68% of organizations can’t accurately identify AI agent activity compared to human activity.

Nearly three-quarters said that agents often receive more access than necessary, and 79% believe they create new access pathways that are difficult to monitor. Just over half said agents, at least occasionally, inherit access originally intended for humans or other systems.

The FBI has shut down 40 laptop farms that were being used for a North Korean fake IT worker scheme.

As part of Operation Lonestar Roundup, 187 computer devices being used by North Korean IT workers were seized, and two people were arrested.

In a scheme labelled Famous Chollima, the fake workers were applying for – and often getting – remote positions by using stolen or fabricated identity documents, VPNs and proxy infrastructure to make it look as though they were working from within the US.

They used US-based co-conspirators to receive laptops from victim companies. These individuals then housed and configured those devices, installed remote-access software and transferred earnings to Korean government‑controlled accounts.

“Operation Lonestar Roundup was aimed at disrupting malicious cyber activity perpetrated by the North Korean government with the purpose of supporting its WMD and ballistic missile programs, intelligence program, and nuclear program,” said FBI Dallas special agent in charge R Joseph Rothrock.

"Each laptop seized potentially represents one company that has hired a DPRK (Democratic People's Republic of Korea) IT worker."

US nationals Michael James Bostelle of Pennsylvania and Juan Cecilio Pena Carrillo of Texas have been arrested for the scheme, which is believed to have generated more than $3 million in revenue. Another dozen people based in China have been charged.

The companies falling victim included organizations in the health care, communications, cyber support, retail, and manufacturing sectors, with the revenue generated being sent to sanctioned entities controlled by the North Korean government.

The operatives appear to have used a network of fraudulent Google accounts to facilitate their criminal activity.

Employers are being encouraged to add accounts listed by the Justice Department to the list of indicators they use to identify and disrupt North Korean IT worker employment.

CrowdStrike, which worked with the FBI to bring the operation down Famous Chollima operatives have been using a range of deception techniques during onboarding. They've filed fraudulent police reports claiming a laptop was stolen to trigger replacement shipments to farm addresses, and rotated through residential addresses to obscure the final destination.

Others have claimed they've been forced to relocate at the last minute to divert devices mid-shipment. In some cases, operatives secured roles through IT staffing firms, which allowed end-client devices to be quietly redirected to farm infrastructure without the staffing firm's knowledge.

This summer, Huntress uncovered at least five North Korean operatives that were part of the Famous Chollima operation and who, between them, were applying for as many as 60 jobs per day.

“This investigation is about far more than fraud,” said US attorney Ryan Raybould for the Northern District of Texas.

“It is about a hostile foreign regime infiltrating American companies through stolen identities and covert access points inside our own borders. Every laptop farm we dismantle shuts down a revenue stream that the North Korean government relies on to fund weapons programs that threaten global stability."

Ugreen's NASync desktop appliances are gaining a strong reputation in the NAS space as they score highly for build quality, hardware specifications, and prices. The premium Ultra series steps in at the top of the range and moves the focus up to growing businesses and power users that want a combination of high storage capacities, top performance, and plenty of features.

This is a review of Ugreen's flagship NASync DXP8800 Ultra 8-bay model, which has a modern Intel Core 7 150U mobile CPU in the driving seat. And eight efficient plus two multi-thread performance cores, a maximum 5.4GHz Turbo frequency, and a minuscule 15W base TDP. Along with a faster CPU, the only other notable difference with Ugreen's older DXP8800 Plus is that the maximum DDR5 memory capacity has been increased by 50% to 96GB.

The DXP8800 Ultra employs exactly the same solidly built aluminium chassis as the Plus model, so you get a veritable port-fest. A standout feature is the pair of 40GbE Thunderbolt 4 USB-C ports, but there are also dual 10GbE multi-Gigabit, USB-A 3.2 Gen2, and USB-A 2.0 ports, along with an integral SC4 card reader, while 8K video output from the CPU's Iris Xe Graphics 96EU chip is routed to an HDMI 2.1 port.

Ugreen NASync DXP8800 Ultra review: Hardware features

The main drive bays use tool-free telescopic carriers and you can pick from a wide choice of devices as Ugreen's compatibility list currently shows 57 different models which include plenty of NAS-specific HDDs from Seagate, Western Digital and Toshiba.

You'll also find two M.2 NVMe SSD PCIe 4 slots lurking behind a spring-loaded cover in the base which can be used as high performance storage pools or caches. Alongside them are two SODIMM slots with one occupied by the base 8GB of 5,600MHz DDR5 memory.

The rear magnetic dust cover has two 12cms diameter cooling fans behind it and during testing, we found they were whisper-quiet. There's also a PCIe Gen4 expansion slot alongside but although the user manual provides clear instructions, patience is required as it's very difficult to access.

Ugreen NASync DXP8800 Ultra review: UGOS and apps

For testing, we loaded four 4TB Seagate IronWolf NAS HDDs and used Ugreen's web portal to discover the appliance and install the latest UGOS software. The web console is easy to use and aids storage pool creation with sage advice on the various RAID options based on the drives selected.

We chose the recommended RAID5 volume and were prompted to create personal and shared folders and configure remote access, which creates a unique Ugreenlink ID and secure web link. We opted for a Btrfs volume to enable storage quotas and snapshots and noted that the latter feature still only supports NAS shares and not iSCSI LUNs.

Ugreen lags behind Qnap and Synology for app choices, but it is catching up as 35 are now available for the DXP8800 Ultra. The Model Manager and OpenClaw agent apps are of particular interest as they bring smart AI tools into play.

Model Manager provides a central location for managing AI features, but Ugreen's AI Console, Uliya, and Voice Memos apps are only supported by the iDX models. For the DXP8800 Ultra, the Model Manager integrates with the Photos and Surveillance Center apps and provides facilities for authorising various AI models for them, including people, text, and image recognition for photos, plus facial recognition and general detection for surveillance.

OpenClaw uses the Docker app and the latest UGOS updates its engine to 29.6.2, which addresses a number of security issues. If you're planning on using OpenClaw, you'll find Darien Graham-Smith's excellent in-depth tutorial in Issue 383 of PC Pro.

Other apps of distinction include Sync & Backup, which secures local data to a remote Ugreen NAS and vice versa, and also supports Rsync servers. The Cloud Drives app provides file syncing for ten cloud storage services including Dropbox, OneDrive and Google Drive, while Ugreen's NAS Windows and macOS desktop tools use sync tasks to secure selected data to shared folders and update it in real time.

The appliance's HDMI port comes into play only when you use Ugreen's mobile app. Load the Theatre app, pick a video to stream, select the HDMI Play projection mode, and sit back and enjoy.

Ugreen NASync DXP8800 Ultra review: 10GbE performance

For our speed tests, we hooked the appliance up to the lab's Zyxel XMG1930-30HP multi-Gig switch and mapped a share over 10GbE to a Dell PowerEdge Windows Server 2025 host. Ugreen added UGOS support for 9000-byte Jumbo frames earlier this year, and with these enabled, we saw good Iometer sequential read and write rates of 9.2Gbits/sec and 8.8Gbits/sec and random speeds of 9.2Gbits/sec and 0.7Gbits/sec.

For our real-world tests, copies of a large 25GB test file between the appliance and server delivered sustained read and write rates of 3.8Gbit/sec and 3Gbits/sec. Securing a 22.4GB folder with 10,500 small files to the share averaged a very respectable 2Gbits/sec.

From the SAN Manager app, we created a 500GB iSCSI target and, after mapping it to our host server, watched Iometer report back with very similar sequential and random read and write rates as the NAS share. We noted that although the SAN Manager clustering feature allows multiple clients to connect to the same iSCSI LUN, MPIO (multipath I/O) for redundant, load-balanced links is not currently supported.

For cache testing, we installed two Kingston M.2 NVMe SSDs and assigned them to our data volume as a mirrored read/write cache. This made significant improvements to random write operations with our NAS share and iSCSI LUN, seeing respective speed increases of 245% and 290%.

Ugreen NASync DXP8800 Ultra review: Is it worth it?

For an 8-bay NAS with dual TB4 and 10GbE ports, the NASync DXP8800 Ultra looks like a good value, with it currently listed on Ugreen's US site for $1,799. Competition comes mainly from Qnap's new TX-h966TX 9-bay hybrid NAS, which sports dual TB4 ports, a 6-core Core i3 CPU, a single 10GbE port, and support for up to four U.2 PCIe NVMe SSDs.

Ugreen's UGOS OS is a work in progress, but all the important apps are present, while the OpenClaw agent and Model Manager show its continued focus on developing AI-powered capabilities. At first glance, it does look to be a modest hardware upgrade over the DXP8800 Plus model, but the Core 7 150U CPU is noticeably faster across the board than the older Core i5-1235U, and memory capacity is 50% higher, so it can easily cope with current and future workload demands.

Ugreen NASync DXP8800 Ultra specifications

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."

Sana is now the default home screen for Workday and will provide the standard experience for all users, the tech giant announced at its Rising event in Las Vegas.

Referred to as Workday Home, professionals can ask the user interface what they need in natural language for key operational activities, such as tasks, decisions, and meetings, and Sana orchestrates secure Workday agents to complete the task.

“For too long, enterprise software forced you to do the work, searching for apps, data, and processes,” said CEO Aneel Bhusri during the keynote.

“We're flipping that script with Sana, the new front door to Workday. Instead of hunting through menus, instead of looking for the work you need to get done, you simply ask, like it's a simple AI prompt.”

The Sana announcement was one of the main events from a keynote presentation that focused on the game-changing potential of AI. As well as its work on Sana, Workday presented a series of new agentic services and platform developments.

The key message from the keynote is that Workday is building agentic capabilities into its platform, rather than bolting them on. With an increasingly open platform and strong links to pioneer models, like Anthropic Claude, Workday wants to help a hybrid workforce of professionals and their agentic colleagues to unlock human capabilities.

“Jobs are going to change; there's no question about it,” said Bhusri, reflecting on the changing nature of work. “Jobs are being redefined into skills and roles that are very different than they were pre-AI. But let's embrace that. Let's all learn AI, use the tools, and embrace the opportunity.”

Workday introduced Work Intelligence, which breaks every job role into tasks. The service automatically connects tasks to the skills they require, showing business leaders the areas that AI can automate, what it should augment, and what parts need to stay human. Professionals, freed up from repetitive work, can then focus on value-adding activities, such as the personal relationships that are crucial to hiring and reskilling in HR.

The company also launched Autopilot Agents that work continuously in the background across hiring, payroll, accounting, and compliance. Once again, these proactive agents aim to free up professionals to work on more valuable activities.

“The question is not whether we should embrace AI or fear it. The question is how do we harness its power and make it safe, reliable, and trusted?” said Gerrit Kazmaier, president of product and technology at Workday during the keynote.

“We can take big steps towards answering this question by transforming HR and finance with AI in a secure, reliable, and in a lawful way.”

High job satisfaction may not be enough for employers to keep hold of their tech staff, according to new research from professional association ISACA, which warns that burnout and unclear career paths are pushing workers to look elsewhere.

According to the organisation's Tech Workplace and Culture 2026 report, which surveyed more than 1,000 tech professionals worldwide, although 76% are satisfied with their current role and 71% are satisfied with their career progression, nearly three-quarters (73%) said they would consider moving to a new employer in the next two years, or had not ruled it out.

Among respondents who had left a previous job, the most common reasons were better career prospects (29%), higher pay (28%), and more interesting work (23%). An unclear path upwards was also the most commonly cited obstacle to career advancement, named by a third (34%) of respondents and 41% of women.

Economic conditions may be masking the scale of this problem, according to Julia Hermann, head of digital platform services at cybersecurity firm Giesecke+Devrient. Speaking at a media briefing during ISACA Europe 2026, Hermann, who is also a leader of ISACA's SheLeadsTech programme in Germany, said layoffs in sectors such as the German automotive industry had led to a phenomenon known as "job hugging".

"People stick to their jobs, though they might not be totally satisfied, because at the moment it's not really easy to find a new job," she said. "The salaries are partially going down, or at least stagnating, so you're not necessarily making a big jump when you go to another company."

Long probationary periods add to the risk of moving, she added: "If you're the last to come, you're the first to go on the next layoffs."

This trend may be delaying departures rather than preventing them, however. ISACA warned that workplace pressures are feeding into the sector's retention problems, which three-quarters (75%) of respondents said were already a concern for their organisation. More than half (59%) of respondents said they had experienced work-related stress or burnout, rising to 64% among women, with the main causes being heavy workloads (48%) and a lack of resources (43%). Despite this, only a quarter (25%) said their employer offers mental health days, and 23% said it offers no support at all.

AI is adding to this pressure, Hermann explained, noting that while the technology is often deployed with the intention to reduce workloads, companies are not hiring for new roles to run the platforms behind it. As a result, she said, the work lands on existing staff on top of their day-to-day operational duties, noting that AI has created "a lot more work" for her team, which is now responsible for managing and securing the internal platforms that support the company's own AI models.

Entry-level roles may be more affected, however. Younger staff tend to move jobs more often, according to Hermann, and AI may also be squeezing the roles they start out in. ISACA's senior vice president of advocacy, Tara Wisniewski, warned this could make the industry's pipeline problems worse, arguing that organisations like ISACA and universities need to help graduates into the market, "and industry needs to be called to the table there too".

"We do believe that professions will change. We don't believe that they will be eradicated as a whole, but we do believe that they will change," Chris Dimitriadis, ISACA's chief global strategy officer, told media at ISACA Europe. He said he expects AI to take on most technical and orchestration tasks, leaving people to focus on governance, configuring and monitoring AI systems, and making final decisions.

In ISACA's State of Cybersecurity 2026 report, which surveyed security professionals globally, soft skills were the most commonly reported shortcoming among security professionals (57%), while 45% cited missing LLM SecOps capabilities, up 12 percentage points from the previous year. As AI takes on more technical work, Dimitriadis said, security staff will increasingly need the ability to explain to the rest of the business, in financial terms, how investment in security and AI will pay off.

Many teams are already short of people and money, however. The same report, published last month, found that more than half of European cyber security professionals said their teams were understaffed (56%) and underfunded (55%).

"Too often we are seeing budgets being sunk into crisis response, but there's still a distinct lack of investment in the workforce, training, and resources needed to prevent attacks and protect organisations in the first place," Dimitriadis said when the report was published.

He warned that the skills gap is likely to widen as roles change, "not just in terms of the numbers, but also in terms of the type of skills that's needed". It could grow "not because we are going to lose the people, but because... their knowledge may become obsolete very fast as digital ecosystems change".

ISACA's Tech Workplace and Culture 2026 survey of 1,025 tech professionals worldwide was carried out between 17 July and 3 August 2026. Its State of Cybersecurity 2026 report surveyed 1,888 cybersecurity professionals, including 494 in Europe, in May 2026.

OpenAI has announced the appointment of Ismail Elmas as vice president of EMEA sales, as the AI giant looks to continue expanding its footprint in the region.

A seasoned industry leader, Elmas arrives with more than 25 years’ experience in enterprise software and cyber security. He joins OpenAI from SpaceXAI via its acquisition of Cursor, where he held the role of senior vice president for EMEA.

Prior to his spell at Cursor, Elmas served as group vice president of international business at Rubrik, and has previously spent four years at Zscaler, where he led teams across Europe and Asia. Other positions include leadership roles at AppDynamics and BMC Software.

In an announcement, Brian McCarthy, OpenAI’s vice president of worldwide sales, praised Elmas as “an exceptional leader” with expertise in building successful teams and helping companies leverage new technologies to drive value.

“He combines the discipline to deliver results with a clear focus on customers,” he said. “I’m excited to work with him as we grow across EMEA and help more businesses across the region put AI to work.”

Expanding OpenAI’s EMEA footprint

The appointment follows a strong period of expansion for OpenAI in EMEA – a region the company considers one of its largest and most important outside of the US. Over the past year, weekly users of ChatGPT in EMEA grew by more than 40%, while ChatGPT Business Enterprise seats across the region have more than doubled in that time.

The last few years have also seen the business add new offices in Dublin, Paris, Munich, Zurich, Brussels, and Abu Dhabi, following the 2023 opening of its first international office in London.

OpenAI said it is now looking to further build on that progress, with new offices in Madrid, Berlin, and Stockholm due to open in the coming months and further expansion plans slated for 2027.

Commenting on his appointment, Elmas said OpenAI has transformed how people work and how businesses conduct their operations.

“The combination of frontier technology and products people already know and use creates an enormous opportunity for businesses across EMEA in this era of agentic AI,” he said.

“Realising that opportunity means being close to customers, understanding their challenges and working alongside them. I can’t wait to build on what the team has already achieved as we enter this next chapter.”

The owner of Phase Eight, Hobbs, and Whistles, TFG London, has confirmed that it has been hit by a cyber attack.

TFG London is the UK subsidiary of South African retail group The Foschini Group, which includes several UK retail brands.

“On Sunday, 11 October, TFG London identified a cybersecurity incident affecting some of our IT systems, specifically impacting our Phase Eight, Hobbs, and Whistles brands. We are actively investigating and working with external cyber security specialists," a spokesperson told ITPro.

"Stores are open as normal but some of our brand’s website capabilities may not be available as the investigation continues. We apologize for any disruption and will provide additional updates to our customers as appropriate.”

Online orders and returns are believed to be affected.

The firm said it has now put its business continuity plan into place, and has reported the incident to the relevant authorities. It said it was contacting all those customers whose personal data might have been accessed, but that there was no need for customers to change their passwords.

"Importantly, payment processing is carried out by a third-party provider," it told customers in an email.

"Based on our investigation so far, we have no reason to believe that payment information is at risk. As our investigation continues, we will be working to identify if any sensitive information related to our customers was impacted."

TFG's African and Australian operations appear unaffected, as are two other brands it owns, Inside Story and White Stuff.

The hack follows similar attacks last year on M&S, Co-op, Harrods and, most recently, ASOS, which last week warned that an unauthorized party had gained access to some personal data, including names and contact details by impersonating a trusted contact to obtain login credentials.

"It may be a different target but it’s the same hack; impersonate a trusted individual such as an employee, a manager, or a contractor and use that to change passwords and privileges to access systems, and potentially steal millions of customer records and personal information," said Clive Summerfield, CEO at FARx.

"One phone call is all it took to pull down M&S, Co-op, and Harrods. This resulted in systems going offline, supply chains in chaos, millions of customer records stolen or compromised, and commercial losses in the hundreds of millions."

It's not known exactly how the attackers gained access, but – as with the ASOS attack – it seems likely that it was through a third-party supplier, perhaps a Managed Service Provider (MSP) or data hosting provider. So far, no cybercrime group has claimed the attack.

"Organizations need to be shoring up their defenses before attackers find a way in, rather than scrambling to contain the damage afterwards," warned Susannah Streeter, chief investment strategist at Wealth Club.

"Retailers are particularly attractive targets, sitting on valuable customer data and relying on interconnected systems to keep everything from online orders to supply chains moving. The consequences of a breach can stretch far beyond stolen information, hitting sales, disrupting operations, and eroding customer trust."

PC sales have fallen yet again as AI-driven price increases continue to bite, according to Gartner.

The trillion-dollar AI infrastructure rollout has led to shortages of key components, notably memory and storage, with one report suggesting AI will use a fifth of DRAM capacity this year.

Gartner said that global PC shipments were 55.4 million in the third quarter of 2026, down 22.4% from the 71.3 million shipped in the same quarter last year. That's on par with shipments from the first quarter of 2023, the analyst firm noted – and the decline is double the 10% annual reduction in PC shipments predicted by Gartner earlier this year.

That fall in shipments is largely down to rising costs of components driving up overall prices, Gartner said, leading to slower sales.

"The impact of higher memory and storage costs is now fully reflected in PC pricing," said Namrata Banerjee, principal analyst for research at Gartner. "DRAM costs surged five times year-over-year in the second quarter, and PC vendors passed those higher component costs on to customers, driving the steep decline in PC shipments during the third quarter."

Scarcity rather than oversupply

While sales have hit similar levels as they were two and a half years ago, the dips are driven by different causes, Gartner added.

"PC shipments have fallen to levels last seen in early 2023, though the market dynamics were noticeably different," said Banerjee. "Rather than excess inventory, the decline in the third quarter of 2026 was driven by higher prices, vendor-led reprioritization of scarce components and a shift toward higher-priced configurations."

The AI-driven components shortage has led PC makers to focus on higher-end models in order to help maintain margins and absorb some rising costs, with a separate report from Omdia earlier this year predicting reduced availability of low-budget devices alongside a wider rise in prices.

Banerjee suggested the absence of budget models was leading customers to hold off upgrading, while businesses focus on AI-focused investments. "The resulting reduction in the availability of lower-cost devices made replacement purchases less affordable for consumers, while businesses focused spending on targeted investments, such as workstations and AI PCs rather than broad-based PC deployments," Banerjee said.

Sales slide across the board

The sales decline hit all of the top PC makers, Gartner added.

Apple saw the smallest fall in shipments at 7.1% between the third quarter of this year and last year, but its PC rivals posted double-digit declines. Market leader Lenovo fell 22.4% over the same period, while second-ranked HP tumbled by nearly a third at 30.9%, and Dell wasn't far behind at 25.4%. Asus and Acer had slower slides at 13% and 11.2% respectively.

The report from Gartner follows a separate report from Counterpoint research showing global smartphone shipments had fallen 20% on-year to their lowest for the third quarter since 2021 – also driven by higher memory costs and supply shortages of other components. Over the summer, Apple admitted that the AI buildout would lead to more expensive iPhones.

Hackers have sparked fires and floods in French supermarkets after targeting temperature gauges in internet-connected refrigerators.

The attackers remotely took over refrigeration systems, resetting target temperatures to 50ºC, leading to fires as well as floods from sprinkler systems, destroying fresh and frozen food.

The hack hit hundreds of supermarkets, including Intermarche, Carrefour, Cooperative, and Monoprix, according to reports. Authorities said an investigation had been started into the "large-scale cyberattack" that targeted the country's food supply.

"Targeting equipment essential to the preservation of fresh products and to the food supply of the French people is extremely serious and is unprecedented. It is unacceptable," CEO of Cooperative U, Dominique Schelcher, wrote on X.

Dutch supermarkets also reported security issues, with some digital systems taken offline at the Albert Heijn chain, while reports suggest supermarkets across Europe may also be affected.

What happened?

The attack began overnight, with temperatures slowly rising over the course of several hours. The hackers appear to have targeted store fridges and freezers via control panels that are accessible via the internet in order to be adjusted and maintained, Reuters reported.

"The attack was probably launched at about 11.00pm but we didn't become aware of it until 4.00am, when our delivery driver saw that the stock room was flooded," said Charles Dezon, manager of the Intermarché supermarket in Gradignan (Gironde), in a report from Le Monde. "The temperature had risen to 50ºC in the cold room and activated the automatic sprinkler system."

Reports suggest the hackers forced the equipment into a constant defrost cycle, leading to overheating.

Known flaw, legacy systems

A source told AFP that the hacked systems appeared to be largely made up of refrigeration control systems from Danfoss, a Danish equipment manufacturer. Danfoss told that news agency that the attack targeted "legacy" systems that no longer received software or security updates, notably the AK-SM800 and AK-SC355 regulator systems that had "outdated firmware".

Danfoss added, in a statement sent to Reuters, that it had "long advised customers to keep supported systems updated with the latest firmware and to migrate legacy systems to current-generation solutions".

Indeed, the attacks come two months after Claroty's Team82 spotted multiple vulnerabilities in Danfoss's AK-SM 800A platform. The vulnerabilities were disclosed to Danfoss, which issued fresh firmware to patch the flaws; however, that appears to not have covered the older version, as it was no longer supported by the company.

At the time, Claroty noted: "A compromised supervisory controller grants an attacker the ability to physically manipulate refrigeration systems and silently spoil the contents."

What's next?

Stores are working to clean up after the incident, with staff spotted chucking spoiled food into bins, but full recovery will likely take much longer — and be expensive. Intermarche CEO Thierry Cotillard told local television that the losses were likely to top "tens ​of millions of euros nationwide in a single day." In the UK, last year's ransomware attacks cost M&S and Co-op up to £440 million.

"The impact on French supermarkets will likely last months," said James Neilson, SVP of Global at OPSWAT. "More than a year on, M&S and Co-op are still feeling the financial and organisational effects of their attacks."

He added: "Refrigeration technicians have to inspect and restore systems site by site, and retailers must reassure customers that what’s back on the shelves is safe."

It's as yet unclear who committed the attacks, but they come a month after French President Emmanual Macron warned that critical national infrastructure needed to be protected from Russian cyber attacks.

Muhammad Yahya Patel, vCISO and cybersecurity advisor for EMEA at Huntress, noted that the security industry has long been warning about attacks on operational infrastructure, saying that these refrigeration systems had been "weaponised". He added: "The food retail sector across Europe should be treating this as a signal that they are being actively targeted."

It is "likely, if not inevitable" that the UK will fall victim to a major cyber attack similar to Australia's recent healthcare breach, AI minister Kanishka Narayan has warned.

His speech comes as part of a government pledge to introduce greater protections against emerging AI threats, use frontier AI to strengthen cyber defences, look at stronger legal protections and make the UK better-prepared for serious incidents involving autonomous AI systems.

"We will use every power at our disposal. We will legislate wherever necessary," Narayan said.

"And we won’t look to law alone. We will look to British ingenuity: our scientific expertise, technological capabilities and institutions to understand these increasingly powerful systems, defend against emerging threats and respond when things go wrong."

There are plans to build on two existing pilot schemes that have already used frontier AI to help identify and fix more than 1,000 vulnerabilities across government systems.

The government is also looking at stronger protections against emerging AI threats with the Cyber Security and Resilience Bill, currently with the House of Lords, looking at how serious AI incidents and near misses can be identified and reported.

It's working with the National Cyber Security Centre (NCSC) and the AI Security Institute (AISI) – which has once again been given access to major US models before they are released.

Naryan pointed out that the UK is increasingly dependent on technologies developed elsewhere.

"Much of the technology that will shape our economy, our public services, our national security is being developed outside this country," he said.

"That is now a question of national security, also a question of democratic power. If the British state cannot exercise meaningful control over the technologies on which our country depends, neither can the people that it represents."

The announcement follows a series of incidents in which AI agents have autonomously hacked into external systems. In one major example, OpenAI admitted that a combination of its models had breached Hugging Face systems, gaining access to the production database.

In a separate incident, four different versions of Anthropic's Claude AI models were revealed to have breached third-party systems.

"To the companies developing the world’s most powerful AI systems, my message is: Listen when your scientists warn of severe dangers. Investigate unexpected behaviours. And if you can’t adequately control the risks, do not release the system," Naryan said.

However, Nuala Polo, UK Public Policy Lead at the Ada Lovelace Institute, said the announcement contains little in the way of definite commitment.

"Stronger protections will be ‘considered’, strengthening the Cyber Bill ‘explored’ and near-miss reporting ‘looked at’, with the detail to come later. There is no new regulatory power, no timetable and no obligation on AI developers," she said.

"Even incident reporting, the most modest measure on the table, is still only under consideration, and might only apply to the ‘victims’ – the operators of essential services – rather than the AI companies."

Microsoft has boosted its Azure Maia chip program, revealing a broad roll-out of its Maia 200 chips and significant expansion with Maia 300 once they come into production in 2027.

According to a blog from Scott Guthrie, EVP of Cloud and AI at Microsoft: “The multigenerational Azure Maia program has become a foundational element of Microsoft’s AI infrastructure strategy.”

Maia 200, which was announced in January and is already in production, is already serving production workloads in Microsoft Copilot, OpenAI models, and the company’s own Microsoft AI (MAI) models. GitHub Copilot and Microsoft foundry are to follow by the end of 2026, said Guthrie.

Maia 300, which will go into production in 2027, was described as “a complete intelligent system” that can support emerging AI workloads like long-context reasoning, latency-sensitive conversational experiences, and sustained, multi-step agentic tasks.

The company claimed that Maia 300 will offer significant improvements on token use efficiency, capable of delivering 40% more tokens per megawatt than “leading GPU-based systems”. It also will deliver up to 7x rack-scale throughput of Maia 200 when in a full 96-accelerator configuration.

Rani Borkar, president of Azure hardware systems and infrastructure, told Reuters: “It's basically delivering more intelligence for every watt, every dollar, and every byte of memory by optimizing across the entire system.”

Nvidia’s customers become its competitors

While it’s not explicitly laid out in Guthrie’s blog, the shift to Maia 200 and Maia 300 means Microsoft is extricating itself from Nvidia’s ecosystem – at least for some of its AI systems.

Maia will be “the primary inference system for Microsoft products and services”, Guthrie said. This position had previously been held by Nvidia chips, notably Nvidia H100 and H200 ‘Hopper’ GPUs and Nvidia Blackwell GPUs.

Microsoft is not alone in taking an ‘in-house’ strategy, either.

Google has its Tensor Processing Units, which it has been developing and using for several years.

Amazon has its Tranium and Inferentia chip family, which is used both by itself and by generative AI pioneers Anthropic, with OpenAI signed on to use Tranium as part of an infrastructure agreement.

Speaking of OpenAI, it’s co-developing its own chips as well, called Jalapeño, with Broadcom. In August, the company said that when compared to “leading commercially available AI systems … (it) delivered a better combination of throughput, power efficiency, and latency”.

“Across all three public models (GPT-OSS, DeepSeek R1, Kimi K2.5), Jalapeño delivered a better combination of performance per watt and latency across the tested operating range, placing it on the Pareto frontier,” the company said.

Meta and Anthropic are also looking into building their own home-grown chips, though there have been no further announcements from either company.

All this doesn’t spell the end for Nvidia, however. Quoted by CNBC, Alistair Speirs, GM of global infrastructure, said the company partners with Nvidia and AMD for training large language models (LLMs).

The companies also have a close relationship when it comes to end-user hardware, such as the upcoming Surface Laptop Ultra.

BlueStar has announced it has agreed to be acquired by TD Synnex, in a move the US-based IT distributor said will generate new opportunities for partners.

Headquartered in Kentucky, BlueStar specializes in technologies across digital identification, point-of-sale (POS), radio-frequency identification (RFID), mobility, digital signage, networking, IoT, and robotics.

The company sells services exclusively to value-added resellers (VARs), integrators, and managed service providers (MSPs) in collaboration with hardware and software vendors.

Post-acquisition, BlueStar said it will continue to deliver specialized knowledge, trusted relationships, and dedicated partner support to the channel and wider IT community.

In an announcement, BlueStar chief executive officer Ryan Girvin described the move as a testament to the "hard work, commitment, and passion" of the firm's employees, customers, vendors, and network of partners.

"Together, we have built a company known for its expertise, innovation, and customer focus," he commented. "We believe joining TD Synnex creates an opportunity to accelerate our growth while preserving the specialized approach and entrepreneurial culture that have defined BlueStar from the beginning."

BlueStar will continue to operate as an independent company until the acquisition closes, with no changes to the processes, programs, and services it currently provides.

Girvin said the firm's success to date has been built on people and relationships – a focus that TD Synnex will help to strengthen through its broader resources and reach.

"We are excited about the opportunities this creates for our employees, customers, and vendor partners, and we are confident that TD Synnex shares our commitment to delivering exceptional value through expertise, service, and innovation," he added.

Expanding resources and capabilities for partners

TD Synnex provides a wide-ranging portfolio of technology solutions spanning cloud, cybersecurity, analytics, AI, and mobility. Supporting more than 150,000 customers across 100 countries.

According to the companies, TD Synnex moved to acquire BlueStar due to its specialized expertise, strong market position, and established presence across key global regions.

After completing the deal, the pair said customers, vendors, and partners can expect expanded resources, broader capabilities, and more market opportunities, alongside the industry expertise and high-touch support already on offer.

"BlueStar's success is built on the expertise of its team and the strong relationships they have developed with partners," commented TD Synnex CEO Patrick Zammit. "We're excited about the specialization BlueStar would bring to TD Synnex, including a focused go-to-market approach that has differentiated the business and created value for its partners."

The proposed deal remains subject to customary closing conditions and financial terms were not disclosed.

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."

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.

AI is significantly under-regulated, Microsoft vice chair and president Brad Smith has told the BBC, calling for greater legal obligations to organizations when their security is breached by AI.

"Right now, your washing machine is subject to more safety rules than AI," he told the BBC's Big Boss Interview podcast. "Every carton of milk that you see in the grocery store is subject to safety rules and inspection."

AI, he said, is both a "tool and a weapon", and AI firms need to implement kill switches for when things go wrong. He also called for a legal requirement for the companies to create incident reports and inform any victims.

Smith also called for new AI technology to be evaluated by independent government bodies – and said that the UK was the best-positioned country to take the lead, referencing the work of the AI Security Institute (AISI).

"I actually think the country and the government that is in the best position to advance AI safety standards and even rules is here, it's the UK," he said.

"In the world of technology, in the tech sector, most people that I know would say, if you ask them, name the country that is the leader in having this technical capability, they would say, it's very simple. It's the UK."

Meanwhile, Microsoft chief executive Satya Nadella has echoed Smith's remarks in a blog post over the weekend.

"It's time to step back and assess the trust architecture for this new era," he wrote. "We simply can't outsource responsibility for what intelligence does on our behalf. A model provider's assurances do not relieve us of that responsibility."

He called for systems to be developed around principles of observability, model diversity, observability, verifiability, and independent auditability. Models should be tightly contained from the start, and incidents should be quickly disclosed.

Notably, Nadella referred to AI as Super Intelligence – Donald Trump's preferred term. Trump, however, has repeatedly said that AI companies should be left to police themselves, and that he doesn't want to see them "stifled".

Late last month, he announced that Anthropic, Google, Meta, OpenAI, Nvidia and xAI had signed a pledge to work with independent auditors on their AI systems - although this was merely "morally binding".

Trump also recently called on AI developers to stop sharing unreleased systems with AISI, causing Anthropic to prevent UK officials from examining Claude Mythos 5.1, its newest model.

However, he has now reportedly reversed that ban, and Smith believes that a UK leading global AI safety efforts could be viewed positively in the US.

"In Washington DC, people don't look at the UK, and they don't look at London the way they look at Brussels – that's a strength," he said.

"Let's speak calmly, maybe even softly, but firmly, and start to bring people together. I hope that's the role we can play as a company. I hope that's the role the UK can play as a country."

The number of ransomware attacks in the UK is rising fast, with two groups in particular identified as the main culprits.

New data from Cohesity has revealed that UK attacks are up 60% quarter-on-quarter and 42% year-on-year. And despite the rising numbers of hackers entering the market, just two hacking groups were behind nearly a third – 32% – of all UK attacks.

One of these is Qilin, a Russian-speaking ransomware group that's believed to be behind the cyber attack on NHS medical services provider Synnovis. This led to the cancellation of thousands of elective procedures and outpatient appointments this summer.

Qilin was responsible for 19% of UK ransomware attacks during the last quarter, according to Cohesity, with data from Rapid7 Labs indicating that it made around $193 million between July 2025 and March 2026.

The Gentlemen, meanwhile, accounted for 13% of all UK attacks, despite only having emerged in July last year. Its victims are mainly found in industrial organizations, the IT sector, and some consumer spaces. Its activity is largely concentrated in Europe, with the UK and Germany among the most heavily targeted countries. Notable victims include Synergy France, UK Electronics, and Equity Life.

And if Mondays aren't bad enough already, they're the most common day for UK ransomware activity, with an average of 71 attacks, followed by 64 per day on Wednesdays.

The most targeted UK industries during the quarter were professional services, with 22 recorded ransomware attacks in Q3 2026, followed by manufacturing at 17 and technology at 13.

The use of AI is becoming increasingly common, with The Gentlemen using AI models in negotiations and data triage, and hacking group JadePuffer demonstrating that an agent can execute an entire intrusion chain without a human operator.

"The biggest change in ransomware is not simply that attacks are becoming more sophisticated. It's that AI is making them easier to scale and harder to predict. Criminals can now use AI to speed up everything from finding valuable data to negotiating with victims, while increasingly automating parts of an attack," said Julien Mousqueton, field CISO at Cohesity.

"For businesses, that means the old assumptions that ransomware attacks follow a linear pattern and require large skilled teams are no longer reliable. Organisations should be putting more focus on protecting identities and stopping attackers from moving through their systems, rather than relying on yet another layer of endpoint security."

But while UK attacks are coming from two groups in particular, overall the number of ransomware groups continues to soar, indicating increasing fragmentation.

There were 109 active groups globally in the third quarter of this year, with 35 new hacking groups emerging, although the top five groups accounted for just over a third of all listings.

And, said Cohesity, geographic diversification is increasing. The US share of recorded ransomware attacks fell from 51% to 34% over the third quarter. Meanwhile, the number of European victims more than doubled, led by a rise in reported incidents in Germany, the UK and Italy.

Overall, global ransomware hit a record high this quarter, up 28% on the previous quarter and 86% year-on-year.

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If you're on the lookout for a mini PC, the chances are you're prioritizing a compact build and affordability over power. While mini PCs can punch well above their weight at times (like the Apple Mac Mini), desktop PCs and workstations still win out when it comes to which machine boasts the biggest performance credentials. In the world of Windows mini PCs around the $500 mark or under, value for money reigns – and it's about finding the most functional machine to suit your likely entry-level needs.

Acemagic's catalogue of mini PCs ranges from the sublime (in the form of the Matrix Mini M1) to the underwhelming (courtesy of the Kron Mini K1). Taking lessons from the failures of the latter is the Chinese firm's newest budget-friendly mini PC, the Kron Mini K5.

Before you ask, we don't understand the naming convention either, but consider this to be the K1's spiritual successor. We have much higher hopes for the newer model given it comes packaged with a new Intel Wildcat Lake entry-level processor, as opposed to one that's several years old upon release. Despite launching in 2025, the K1 featured the then-three-year-old Intel Core i5-12600H CPU, for reference. With fresh hardware and a cool blue chassis, can this $400 (after discounts) mini PC make a case to be the best entry-level machine of its kind out there?

Acemagic Kron Mini K5 review: Design

This mini PC takes a subtle left turn from the familiar design language of its predecessors, manifesting as a couple of tweaks alongside a knockout color choice that gives the machine some extra character. Although its compact dimensions remain the same as the Kron Mini K1 (128 x 128 x 41.3mm) and even the Matrix Mini M1, the Kron Mini K5 feels modern; the cool, breezy exterior complements the fact that its components are fresh.

You can also find this design language in the etched lateral metallic strip lining the top of the machine, matching the tone of the metallic blue used on top, which contrasts excellently with the matted and dustier iteration on the rest of the body. This is enough to add something different to a neat but overly familiar design. Otherwise, the power button has changed – spelling out 'POWER' instead of the power symbol we're familiar with. Thankfully, the LED backlighting remains. You may well not care about all this if you simply want to mount the device onto the back of your monitor – with the tools to do so provided in the box – but leaving the Kron Mini K5 out on your desk is no bad thing and gives you something easy on the eye to add to your workplace aesthetic.

The selection of ports available is identical to the Kron Mini K1, with the front hosting the only USB-C port (3.2 Gen 2) alongside two USB-A 3.2 Gen 2 ports and a 3.5mm headphone jack. The rear features four USB-A 3.2 Gen 1 ports, DisplayPort 1.4, HDMI 2.0, a 2.5GbE Ethernet port, and DC input. There's also room for a Kensington Lock Slot (as well as a lock switch at the top of the rear), but sadly no SD Card reader, which you may often find on the side of machines like this. Your wireless standards, meanwhile, are Bluetooth 5.2 and Wi-Fi 6, both of which aren't the newest out there.

Acemagic Kron Mini K5 review: Specs and performance

The Intel Core Ultra 5 320 Wildcat Lake processor fitted into the Kron Mini K5 is a six-core entry-level chip geared for energy-efficient budget laptops and mini PCs. Two performance cores (for more demanding workloads) have a 1.5GHz base clock speed (and 4.6GHz turbo), while four efficiency cores (for more background and everyday tasks) offer a 1.3GHz base clock speed (and 3.4GHz turbo).

The chip also integrates around 16TOPS of AI performance, which isn't much – but enough for Windows 11's simple AI features like real-time background blurring on a video call, live noise cancellation, as well as real-time translation or transcription. It also comes with an Intel Xe Graphics chip, 16GB LPDDR5 RAM, and a 512GB SSD.

Performance testing with Geekbench 6 produced a single-core result of 2,427 and a multi-core score of 7,655. The former is largely in line with expectations and means that it can handle all simple, single-core background tasks as well as you would need. The latter result also met our expectations – performing much better than the Intel Core i5-12600H in the Kron Mini K1 (5,508) as well as the Intel Core Ultra 5 125U in the Acer Revo RB-102 (6,869), which actually costs a little more.

Graphics performance too was very good; it clocked a score of 12,658 in the OpenCL test, beating both the Acer (11,029) and the Kron Mini K1 (9,630). Last year's Matrix Mini M1, however, did beat it with 13,514. The Intel Xe Graphics chip won't come anywhere near the Intel Arc Graphics chips, but should be enough for basic photo editing and graphic design.

Unfortunately, if you're hoping to transfer data at speed – SSD reads of 1,775MB/s and writes of 1,689MB/s are certainly on the lower end and well below average.

Acemagic Kron Mini K5 review: Is it worth it?

We enjoyed plenty about the Kron Mini K5 mini PC, including its fresh and sharp design and metallic blue double-hue chassis – not to mention the fact that the components powering it are as new as you can get. This might sound like an obvious point, but plenty of mini PCs out there in the budget category are retrofitted with chips that are several years old at the time of release, so to find one available for roughly $400 with a new Intel chipset and enough AI power for basic on-device processing is excellent. You'll also benefit from a healthy amount of graphics power at this price point.

There are some misgivings, such as the wireless standards being a little behind the times, as well as a port selection that lacks more than one USB-C connection and no SD Card reader. The SSD could also have done with a bit more speed. But it's certainly enough to get by for the majority of office workers – and it won't break the bank.

Acemagic Kron Mini K5 review specifications

Former Sophos chief security officer (CSO) Simon Reed has joined Overe as a strategic advisor in a move CEO Paul Barnes believes will supercharge engagement with managed service providers (MSPs).

Launched three years ago, Overe offers a cybersecurity platform designed to help MSPs and small businesses secure Microsoft 365 and SaaS tools.

Speaking to ITPro, Barnes said the inspiration for launching the firm came after his experiences with a previous venture, endpoint security startup Prevx. At its height, before being acquired by Webroot, Prevx had more than 15,000 MSPs using its product.

With Overe, Barnes hopes to build on that success and bolster support for what he said has traditionally been a market overlooked by larger vendors.

“We’re very much focused on the MSP market because that’s been something which I’m extremely passionate about,” he told ITPro. “We know how underserved that market is and those clients which the MSPs serve.”

According to Barnes, the state of the security vendor market in 2026 requires a rethink. Large organizations such as Microsoft, for example, typically focus on larger clients, leaving a vacuum where smaller businesses are “kind of left to manage themselves”.

Combined with traditional challenges such as costs and skill capabilities, this places organizations at great risk amidst an increasingly perilous threat landscape, he added.

“Typically an MSP client is only small, like 10 users, clients, and the technologies that they are there to try and support them [with] are either far too expensive or far too complex, [and] hard to manage,” he told ITPro. “They make it inaccessible in the sense that you need expertise to really take advantage of that.”

“There are capital costs, staff, and the actual buying of the technologies,” Barnes added.

With Reed joining Overe, Barnes said this marks a “big win” for the company. The industry veteran brings a wealth of experience, having come from a software engineering background before moving into the cybersecurity industry in 2001.

Working on the vendor side with roles at McAfee and Sophos, this gives Reed a unique insight into this market, Barnes said.

A challenging environment

Speaking to ITPro, Reed said his appointment at Overe comes amidst a challenging period both for MSPs and end-user enterprises, many of which lack key technical capabilities and require continuous support.

“It used to be that cybersecurity was an install and forget capability,” he said. “The product updated itself and kept you protected. That’s not the case these days you need continued expertise in partnership with [a] decent product and capability.”

“If you think about a smaller company, where are they getting their cybersecurity expertise? Generally, they’re getting it from the MSP, and it’s that partnership between product and the MSP that’s the most crucial link.”

Combined with rising cybersecurity threats and the advent of generative AI, these dynamics are placing huge pressure on both enterprise end-users and MSPs alike, according to Reed.

It’s here that Overe aims to plug gaps and provide support, with a particular focus on cyber resilience. Indeed, it’s this focus that drew Reed to the company in the first place.

“We’re at a sort of sea change in thinking about cyber where just building defenses is just not adequate on its own,” he told ITPro. “You will see people working a lot hard on what I call resilience in that market

“This is why I’m passionate about it [Overe] because it’s not just a sort of defensive play for MSPs. It’s a resilience play. It’s about have you got your estate configured, have you got this tool set up, do you have the coverage in the place you think you have the coverage,” he added.

“It plays into that growing need in the market for more resilience, and we definitely need more resilience in smaller medium businesses.”

Looking ahead, Barnes said Overe intends to build on early success so far and the company has built a “solid team” that operates in several regions.

“We’ve got some great early angels and built a solid team around us,” he said. “We’re in a few regions and having someone like Simon advise us it’s a massive win for us.”

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The UK and Germany have announced a new Industrial Tech Corridor aimed at encouraging partnerships between companies in the two countries.

The deal, which builds on the existing Strategic Science and Technology Partnership, is being backed by some of Germany’s leading companies including Allianz, Bosch, RWE, and Siemens.

The aim is to connect industry leaders, tech firms, and innovators from both countries, allowing them to work together on AI and critical technologies.

"Together with the UK, we’re building an ideal launchpad for AI companies to scale internationally," said Germany's federal minister for digital transformation and government modernization, Karsten Wildberger.

"The UK-German Tech Corridor will accelerate scale-ups to enter Europe’s most advanced AI ecosystems. This partnership represents an important step to strengthening our digital sovereignty."

The focus will be initially on AI and high-value industrial applications, with a first dedicated Tech Corridor roundtable convening CEOs of selected primes, AI scale-ups, and senior officials scheduled for later this year.

To connect AI and critical technology scale-ups with industrial demand, firms in both countries will be encouraged to develop scaled industrial use cases.

The two countries will also share a pot of around €20 million to help accelerate joint quantum research. This will support projects in a range of areas, from quantum computing through to the technology’s use in financial services.

It could also lead to cutting-edge breakthroughs in areas such as quantum sensors.

This means cash for a further five research projects across Germany and the UK, making use of the existing bilateral quantum cooperation and increasing the number of funded projects to 10.

"We already work closely with Germany in key areas like AI and quantum, and today lays a new foundation - combining the strengths of our businesses and innovators so people in both countries will benefit from for years to come," said UK secretary of state for business, innovation, science and trade, Jonathan Reynolds.

MicroAGI announces London hub

Following the deal, Germany-based tech firm MicroAGI, a robotics research company headquartered in Munich, will expand its footprint in London, creating 180 new jobs, while energy company Proxima Fusion plans to expand its base in Oxfordshire, hoping to quadruple its local workforce by 2028.

The UK-based AI software company Quantexa, meanwhile, is committing to building a dedicated sovereign unit in Germany which will support AI deployment in both countries.

Similarly, the two countries have also convened senior business leaders for a Regional Growth Forum, focused on fostering business-government collaboration, regional growth and innovation.

The plan is to develop a deeper regional dialog to be held in 2027, involving local and regional stakeholders.

"Today’s agreements are great news for British tech, businesses, and workers. They mean more jobs, more investment, and more innovations that will change lives," said Reynolds.

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Keeper Security has announced it now protects more than 100,000 enterprise customers worldwide, as the identity security vendor continues to build on strong recent growth.

The zero trust and zero-knowledge specialist said the growing adoption of its security platform spans all industries – including finance, healthcare, telecommunications, manufacturing, and the public sector – across more than 150 countries.

The latest milestone follows the news that Keeper Security has now surpassed $240 million in annual recurring revenue (ARR), while research firm Gartner named the company as the second fastest-growing competitor in the worldwide security software market in 2025.

The firm’s KeeperPAM privileged access management platform unifies capabilities such as password and passkey management, secrets governance, privileged session monitoring, endpoint privilege management, zero-trust network access, and remote browser isolation.

The platform’s zero-knowledge architecture works to ensure that all credentials and session data remain encrypted with the customer’s private key to give security teams complete control over their data.

Driving enterprise adoption

Fresh innovations have helped drive adoption of the platform across the enterprise – including the introduction of KeeperAI. According to Keeper, the tool provides real-time session monitoring to reduce threat detection time by 99% with automated response across 100% of privileged sessions.

The vendor has also extended agentic AI governance to its Endpoint Privilege Manager to enable enforcement of access policies, elevation controls, and behavioral monitoring across known and unknown AI agents.

Additionally, the Keeper Secrets Manager now integrates with model context protocol (MCP) servers to enable secure credential access for AI agents, while certified enterprise connectors for platforms such as Microsoft Azure Logic Apps aim to eliminate hardcoded secrets within automated workflows.

Elsewhere, Keeper said system integrations with SailPoint and Wiz are also helping to accelerate adoption, alongside the integration of privileged access and and credential approvals into platforms such as Microsoft Teams, Google Chat, Slack, Jira, ServiceNow, and Freshservice, which enable users to request and approve access without switching systems.

Consolidating security in the agentic AI era

Commenting on the latest growth milestone, Keeper Security CEO and co-founder Darren Guccione said the achievement comes amid a “paradigm shift” in the market, driven by the rise of AI across the enterprise.

"Forward-thinking security leaders are strategically moving beyond disparate, siloed software in favor of a unified identity control plane that offers unparalleled visibility, control and governance,” he explained. “They recognise that identity has become the primary attack surface, and platform consolidation is a strategic imperative.

“Keeper's architecture was designed to address this complex reality, and our growth demonstrates that enterprises are decisively embracing this new direction."

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The US Justice Department and FBI have seized the domains of a company linked to the Flax Typhoon cyber crime group, disrupting access to two hacking tools.

The tools, Microscan and FishHub, were used to scan and compromise critical infrastructure systems and other networks in the US and around the world.

Integrity Technology Group, a China-based company with contracts with the Chinese government, is believed to be behind the activity, which has been linked with campaigns known as Flax Typhoon, Ethereal Panda and Red Juliett, amongst others.

The threat actors use a unique combination of large-scale botnets, VPN infrastructure, living off the land (LOTL) techniques, and repositories of computer network exploitation (CNE) tools.

“Integrity Technology Group provided China-linked threat actors with capabilities used to conduct widespread vulnerability scanning and, in some cases, intrusions targeting US and foreign critical infrastructure,” said assistant director Brett Leatherman of the FBI’s Cyber Division.

“The PRC relies on contractor and enabling companies to expand the reach and scale of its malicious cyber activity. By exposing and disrupting these enablers, we make it harder for the PRC to target American networks and infrastructure.”

According to US officials, Integrity Tech developed Microscan to conduct reconnaissance, via the botnet and otherwise, of victim computer networks for vulnerabilities that its clients would later exploit.

A Python-based web application, Microscan contains over 1,300 penetration testing scripts written to scan websites for specific vulnerabilities. The threat actors used these scripts to target services including OpenSSL, Oracle WebLogic, Rejetto, WordPress, Juniper ScreenOS, Jenkins, and Apache Struts.

Targets included a US power company based in South Carolina, a multi-national NGO, Japanese and Polish airports, Taiwanese critical infrastructure companies in the natural gas and power sectors, and two Taiwanese universities.

Spear phishing campaign targeted corporate networks

Meanwhile, a second tool, FishHub, is believed to have played a part in the exploitation of computer networks through spear phishing.

After an initial network compromise, FishHub downloaded additional malware to the victim network, giving Integrity Tech’s clients unauthorized remote access. It also searched for specific files and sent them to servers controlled by the group. Confirmed victims included around 20 Taiwanese universities.

"The extensive malicious cyber activities, and services by Integrity Tech, that have been exposed today should be extremely concerning for all network defenders," said Paul Chichester, director of Operations at the UK's National Cyber Security Centre (NCSC).

"The breadth of sectors that have been targeted across the globe demonstrate the extent of the threat and all organizations should take note of this warning."

Last year, the UK government sanctioned Integrity Tech, alongside another China-based information security company, known as i-Soon, for their part in malicious cyber activity.

“These state-sponsored hackers continue to aggressively target and access networks and systems throughout the world in an effort to identify and steal files and otherwise exploit victims’ vulnerabilities,” said attorney Troy Rivetti for the Western District of Pennsylvania.

“These seizures, our second disruption of Integrity Tech’s massive operations in as many years, send another clear message to cybercriminals from the PRC and elsewhere of the department’s dedication to defending and maintaining cybersecurity in the United States and abroad.

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The new Acer Aspire Go 15 is the first laptop released in the UK to run on Qualcomm's new Snapdragon C chip. We've used the word 'new' twice in that last sentence, and in both cases it's doing a fair bit of heavy lifting.

First, the Aspire Go 15 has been around for a while, running on a selection of low-power processors like the Intel Core 5 120U and AMD Ryzen 7 5825U; second, the Snapdragon C is, to all intents and purposes, a rebadged Dragonwing QCS6490, a processor that's been knocking about since 2021.

The Snapdragon C is a 7W TDP chip that features 8 cores, but they are the standard ARM-pattern Kryo cores rather than the custom Oryon cores found in the X1 and X2 processors. Graphics are handled by an Adreno 643 GPU, and there's a 12 TOPS Hexagon NPU in the mix.

Given that information about the chipset, and the fact that this is a laptop with a £449 price tag, which makes it the cheapest model in the budget Aspire Go range by some margin, it should be clear that we are here looking at the very entry level of the laptop market. Ladies and gentlemen, please limit your expectations.

Acer Aspire Go 15: Design

For a 15.6in laptop, the Aspire Go 15 is relatively light and compact, weighing 1.21kg and measuring 356 x 235 x 20mm. That makes it easy to slip into a backpack and carry around for the day, even with the small bundled 65W USB-C charger. The body is made entirely from plastic, but it feels reasonably solid and looks rather more expensive than it actually is. In the absence of any metal, the lid is rather bendy, though not excessively so. The silver paint finish does an excellent job of not showing fingerprints.

Wide screen bezels usually give the game away with budget laptops, but the Aspire Go does well here with plastic strips just 6mm wide at the sides, 10mm deep at the top and 15mm at the bottom. On the left side of the Go 15, you'll find two 5Gbps USB-C ports and an HDMI 1.4 video output. On the right is a single 5Gbps USB-A port and a 3.5mm audio jack.

Traditionally, Acer's Aspire Go machines use a barrel DC-in jack, but this new model can be charged via either of the Type-C ports, both of which also support DisplayPort video output.

Remove the plastic base plate and the first thing that strikes is the vast amount of space: the entire upper left quadrant is unoccupied save for a single small fan. Next to the Serengeti-esque void is the motherboard, while below sits a 52Wh battery that's easy enough to remove. Everything of interest is fixed in place, so there's no opportunity to add or change the RAM or storage or swap the WLAN card.

The Qualcomm C6700 WLAN card supports 6 GHz Wi-Fi 6E and Bluetooth 5.4, which is absolutely the most you can expect at this sort of price.

Acer Aspire Go 15: Keyboard, touchpad, and webcam.

The keyboard benefits from an expansive layout with a handy numeric keypad and full-sized left/right arrow keys. The keyboard deck could be rather more solid; there's quite a bit of sag when you press down hard in the middle, but the keys have a positive action, albeit over a somewhat short travel. The keyboard lacks anything in the way of a backlight, but given the MacBook Neo is in the same boat, we can't mark the Acer down on this score.

The 125 x 80mm plastic touchpad beneath it is not too bad either: It's smooth and responsive with a well-damped and quiet click action. The webcam may be a 1080p rather than 720p unit, but it lacks support for Windows Hello facial recognition, and the images it produces, while bright, are also colorless and a little hazy. At least Acer has seen fit to install a manual privacy slider.

Acer Aspire Go 15: Display

We were happy to discover that the 60Hz 1,920 x 1,080 display is indeed an IPS panel rather than some bargain-basement TN affair with all the visual refinement of an Etch A Sketch. The viewing angles are wide, and the anti-reflection matte finish does a very good job keeping reflections in check.

In the debit column, the maximum brightness is a somewhat Stygian 268cd/m2 while color gamut coverage is meagre to say the least, with volumes of just 55.2% of sRGB and 39.1% of DCI-P3. With so little color, measuring the accuracy was a waste of time, but we did it anyway. The Delta E variance against the sRGB profile came back at a woeful 6.8.

Ignoring the fact that Lenovo can sell its Chromebook 14 Plus with an OLED screen for the same price, we expected little more on the display front, and in the cold light of day the Acer's display is entirely suitable for basic PC duties. Watching some shows on Netflix wasn't as dreary an experience as the colourimeter numbers suggested it would be.

Acer Aspire Go 15: Specs and performance

The 'new' Snapdragon C processor contains one high-performance core running at 3 GHz, three more performance cores running at 2.4 GHz, and four low-power cores running at 1.9 GHz. There's also 8GB of single-channel RAM.

If you're expecting performance to match the new £700-£800 budget laptops that are arriving on the market pell-mell at the moment, you'll be disappointed. There's a good reason for that £250-plus price difference.

The gulf in performance between the Snapdragon C-powered Aspire Go 15 and the likes of the Wildcat Lake-powered Acer Swift 14 Air, the A18 Pro-powered MacBook Neo, and the Snapdragon X1 Asus Zenbook 14 is considerable.

Starting with the results of our trusty old Hanbrake-based 4K multi-media benchmark, the Aspire Go 15 scored a lowly 38 points compared to the Swift Air's 167, the MacBook Neo's 120 and the Zenbook 14's 190. That's a gap so large you could fill it with water and reenact the Battle of Trafalgar.

Looking at the Geekbench 6 multi-core CPU benchmark results, the Aspire Go's score of 3,213 isn't great, less than half the 7,992 scored by the Swift Air, but it is better than the quad-core Intel N150 processors in the Beelink Mini S13 and Acemagic V1, which scored 3,023 and 2,750, respectively.

For reference, other CPU benchmark scores include a Geekbench 6 single-core result of 1,181 and Cinebench R24 scores of 212 multi-core and 57 single-core.

Overall performance was indeed similar to the Intel N150-based Beelink Mini PC, which scored 50 points in our 4K multimedia benchmark; in other words, fine for the most basic of web-based SaaS duties and media consumption but little beyond.

Drilling down into the Aspire's performance, it's clear that what really lets the side down isn't so much the CPU's performance as it is the GPU's capability, or lack thereof.

The Geekbench 6 benchmark ran so slowly that after 90 minutes we gave up on it, but the 3DMark Night Raid test returned a score of 4,980. Compare that to 18,140 from the Swift Air's Intel Graphics iGPU.

When we undertook some basic creative work on the Aspire Go 15 using Gimp and HandBrake, rendering proved to be very slow, it did at least run all the jobs we tried without any hiccups or crashes.

The 12 TOPS Hexagon NPU is of little value in the Windows scheme of things: the only part of the Copilot+ panoply that's supported is Live Captions. Everything else, including webcam Studio Effects, Generative Fill in Paint, and Recall, is missing.

Storage comes in the form of a 128GB UFS 3.1 card made by Biwin. Average sequential read and write speeds came back at 1,678MB/s and 1,295MB/s respectively, which is not at all bad and certainly faster than the rest of the chipset can make any real use of.

It would have been nice if Acer had started the storage capacity at 256GB. With just half that, users will constantly be having to housekeep Windows to remove any unnecessary bloat just to keep a usable amount of free storage space.

While Windows 11 runs perfectly well on Snapdragon C, we couldn't help but think that a lightweight Linux distro like Manjaro would be more suited to such a low-power platform. Sadly, we weren't able to boot the Aspire Go from a USB stick, no matter what we tried.

Qualcomm boasts that the QCS6490 offers "Long-term support for Android OS upgrades, Linux, Ubuntu..." so assuming you can get past the boot issues we experienced, Linux should run.

Qualcomm's media pack makes much fuss about the efficiency of the Snapdragon C platform, and here it does indeed do well. In our standard video rundown test, the Aspire Go ran for 18 hours and 2 minutes.

That's a thoroughly good showing for a super-budget laptop and more than double what you can expect from any of the Intel- or AMD-driven Aspire Go 15 laptops.

An obvious benefit of having a 7W CPU is that heat generation is simply not a problem. Even the tiny fan barely has to do any work to keep things cool. In the vast majority of user scenarios, the Aspire Go 15 may as well be passively cooled.

Acer Aspire Go 15: Is it worth it?

It's hard to get overly excited about the Aspire Go 15, given its utilitarian ambitions. After all, for £450, all you can reasonably expect is a laptop that can handle the most basic computer tasks. And while the Aspire Go 15 can absolutely do that, it can't do much more.

If we temper our expectations, the Aspire Go 15 shines more brightly. The IPS display is arguably the weakest component, but it's perfectly usable and suffers from none of the problems that bedevil cheap TFT panels, while the sound system is surprisingly competent.

It also bears remembering that even such basic functionality as charging over USB-C, 6GHz Wi-Fi and a decent battery runtime are by no means given on super-budget laptops in this sort of price bracket.

Performance isn't desperately awful either, despite the less-than-impressive benchmark scores. If all you want a laptop for is basic productivity, web browsing and media consumption, the Aspire Go 15 won't give you any reasons to regret parting with £450.

Acer Aspire Go 15: Specs

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?

Computing pioneer and the originator of software engineering, Margaret Hamilton, has died aged 90 years old.

Hamilton is perhaps most remembered for her work coding Apollo 11 to the moon, made famous in part by a photo of her standing next to a print out of the code she created.

Her legacy, however, extends far beyond that role. Anyone who has ever called themselves a software engineer owes that job title to Hamilton.

In 2017, she told Spanish newspaper El País: “I fought to bring the software legitimacy so that it – and those building it – would be given its due respect, and thus I began to use the term 'software engineering' to distinguish it from hardware and other kinds of engineering, yet treat each type of engineering as part of the overall systems engineering process."

“When I first started using this phrase, it was considered to be quite amusing. It was an ongoing joke for a long time. They liked to kid me about my radical ideas. Software eventually and necessarily gained the same respect as any other discipline,” she said.

Hamilton also pioneered defensive programming, a development practice of creating software that can anticipate or fix errors on its own.

Speaking to ITPro in 2016 about the spark that ignited her interest in software reliability, she said: "When the computer crashed during the execution of your program, lights would start flashing, bells would start ringing and developers and computer operators would come running to find out whose program was doing something bad to the system. I didn’t want it to be mine!"

A prime example of this in action occurred during the Apollo 11 mission, when the computer in the lunar landing module, Eagle, experienced a 1202 error, indicating there was a fault in the hardware switch.

This fault led to the system becoming overloaded and there was a possibility that, with just three minutes to go until touch down, it wouldn’t be able to handle the landing procedure.

The software Hamilton and her team had developed, however, was able to prioritize mission critical tasks and shut down any unnecessary background tasks, leading to a successful landing.

An early tech entrepreneur

Hamilton was also an early computing entrepreneur, leading an MIT spinout after the Apollo missions were wound up and founding two tech startups.

Her final endeavor was Hamilton Technologies, which she founded “to provide products and services to modernise the planning, system engineering, and software development process in order to maximise reliability, lower cost, and accelerate time to market”.

It’s perhaps best known for developing the systems modelling language and methodology Universal Systems Language. Designed for engineering complex software systems, it too is built on Hamilton’s philosophy of defensive programming and priorities error prevention.

Universal Systems Language may not be as common as other coding languages due to its specificity but it’s still in production today, more than 30 years after its initial creation.

Hamilton’s legacy will be felt by the technology community everywhere.

Olivier de Weck, the Apollo Program professor and interim head of the MIT Department Aeronautics and Astronautics, told MIT News: "To say Margaret Hamilton was a pioneer -- to say she was ahead of her time -- would be a dramatic understatement. She was a software engineer at a time when that field was in its infancy, and she not only developed advanced code herself but also led a team in using that nascent technology to develop one of the most complex systems humanity had ever achieved."

"[She] has been an inspiration to generations of computer scientists and engineers. Hers was a career dedicated to preventing errors and what she called 'handling the unknown,'" he added. "She personified leadership by example, and established a practice of software engineering based on problem-solving and systems engineering that we all benefit from."

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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.

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.

IBM and Red Hat have fixed more than 400 previously unknown vulnerabilities in widely used Java libraries through their Lightwell Clearinghouse initiative.

Announced earlier this year with a $5 billion investment and more than 20,000 engineers, Lightwell Clearinghouse lets enterprise customers submit open source software dependencies for priority review and fixes.

"For over two decades, Red Hat has backported security patches across thousands of packages," said Matt Hicks, president and CEO of Red Hat.

"Lightwell scales this exact model across a wider scope of open source ecosystems. We are applying the same discipline, upstream commitment, and engineering rigor across all active application layers."

Since the initial Lightwell launch, the duo said they have uncovered, remediated, and backported fixes for hundreds of previously unknown bugs in widely deployed, production-grade software.

"Finding and neutralizing 400-plus novel vulnerabilities so quickly shows how fast Lightwell can move, and we are just getting started," said Gunnar Hellekson, vice president and general manager, Lightwell, Red Hat.

IBM and Red Hat’s combined capabilities

Lightwell is designed to make the most of the two firms' open source engineering expertise, together with Red Hat’s secure software supply chain capabilities, build infrastructure and open source community relationships.

The project uses AI-assisted engineering workflows, although AI-generated patches aren't automatically distributed to customers but are first tested and validated by humans.

It develops version-specific fixes for open source application dependencies in production systems that are delivered through secured repositories that connect with customers’ existing IT processes.

This, the two firms said, allows organizations to address difficult or previously unknown vulnerabilities without needing to replace their current security scanners, software repositories, development pipelines or testing processes.

Through Lightwell Network, IT teams can access verified patches, bring remediated software into their existing workflows and establish an ongoing process for addressing vulnerabilities.

Broader open source gains

The fixes that are developed through Lightwell are contributed to upstream open source projects under responsible disclosure protocols, said the firms, maintaining embargo protections for Lightwell Clearinghouse participants.

Lightwell Network launched commercially in July, with more than 6,500 patched and digitally signed software dependencies for Java, Python, and other languages. Universities, non-governmental organizations and think tanks were given free access in August, and it's now been made generally available.

"AI agents shifted the threat landscape overnight, exploiting old dependencies at machine speed. They do not care if a codebase is ten years old or otherwise considered stable, because one small crack is all it takes to chain an attack together," said Hellekson.

"Finding those bugs is only half the battle: the real work is backporting fixes directly into active production apps so customers do not have to pick between security and uptime."

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Google is expected to introduce full access permissions for Gemini that would allow it to access any file on a macOS device, open apps, browse the web, and perform actions without asking for permission every time.

As spotted by AI news tracker TestingCatalog, the permission has been included in a new "Additional sandbox options" setting.

It allows Gemini to read, create, modify, or delete files residing anywhere on a Mac, including files outside connected folders and even files belonging to other people stored on the device.

Data could be sent and received over the network without approval for each connection, including accessing websites, APIs, and services that the user is logged into.

Notably, it could also communicate with other Mac applications such as Mail, Safari, or Messages, and carry out actions through them without specific permission.

"Gemini will still ask user permission before purchasing products, creating accounts, accepting legal terms, or modifying sensitive information about you," the report reads.

Deeper AI integration

The news highlights the lengths to which vendors will go to gain access to high-quality, up-to-date and unique AI training data, according to Ilia Kolochenko, founder of ImmuniWeb.

"Today, 99% of websites, online archives and web libraries have erected technical barriers to ban AI data scrapers, leaving AI vendors without access to free data,” he said.

“We will almost certainly see all major AI companies following Google and collecting their AI training data via various 'creative' techniques.”

Kolochenko added that access to training data is an “existential question for all Ai vendors,” but most will “probably accept the risk”.

The situation is even riskier for smaller vendors, he noted, many of whom are limited by funds and other resources to procure training data for in-house AI models.

"They form the so-called AI training pools, where hundreds of smaller players submit some data and, in exchange, can use all other data from the pool," he said. "Eventually, once you share your data with a small AI startup, it may end up in thousands of wrong hands around the globe."

Apple's AI security focus

The news comes as Apple tightens up Mac security for AI agents, announcing plans to introduce additional controls that will require Mac users to take "very explicit" action to enable Full Disk Access.

"Some developers are using Full Disk Access in ways that could put users at risk, exposing everything on their systems —including files, mail, messages, and even browsing history —without users’ full knowledge and understanding," said the firm.

"Going forward, we will introduce additional controls to ensure that users who genuinely wish to grant an app this extraordinary level of access can only do so with very explicit user action."

The new Gemini feature hasn't yet been enabled, and it's not clear when Google might plan to do so.

ITPro approached Google for comment, but did not receive a response by time of publication.

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The use of AI in financial departments is surging, but workers now find themselves spending a quarter of their week verifying or correcting AI outputs.

That's according to Datarails' 2026 CFO Sentiments Survey, which focused on the impact of AI on finance departments.

Datarails found that nearly all of the 270 chief financial officers (CFO) polled were using AI in some way for finance processes. While adoption is no longer a concern, the study noted that verification burden is.

Around 96% of CFOs said they spend at least 10% of their time checking or fixing AI outputs, with 8% spending more than half their working time on such efforts.

More widely, finance teams are spending 26% of their working week checking AI output to ensure it's accurate, the survey found.

Crucially, the study noted that they're right to check. Two-thirds of CFOs said their most common source of frustration with AI is when it gives confident answers based on incorrect data.

More than half (56%), meanwhile, said they've had AI provide different answers to team members despite using the same prompt and data.

"What this survey shows is that although AI adoption is now standard for CFOs, caution hasn't disappeared," said Didi Gurfinkel, CEO and co-founder of Datarails. "Finance teams have stopped asking 'will we use AI?' and started asking 'how am I going to most effectively check its work?’”

Keeping humans in the loop

Despite concerns about AI slop and inaccuracies, more than half of CFOs expect to increase their spending on AI licenses this year, with just 7% slashing budgets.

Three-quarters feel under ‘high’ or ‘very high’ pressure to integrate AI further, but just 7% said their finance department workflows are ready for a wider implementation.

Given such challenges, it comes as no surprise then that finance leaders value a human in the loop, with just 5% saying they'd hand an AI-produced financial report to the board without human review, and just 4% saying they'd do the same with monthly reports.

AI isn’t impacting jobs

The survey also asked CFOs about job losses, a recurring talking point since the advent of generative AI nearly four years ago.

Yet only 3% of respondents said they were cutting full time employees due to a shift to the technology – perhaps not a surprise given checking outputs remains manual work.

"Fears of job losses have been largely allayed, but the challenge of AI output verification is critical," said Gurfinkel. "Tackling it will allow finance teams to spend less time doing intensive checking, and let them focus instead on the strategic work they were actually hired to do."

A lack of auditability remains the main reason CFOs don't trust AI tools with mission critical tasks, the report found, with 75% of those asked naming it the biggest challenge.

That was followed by accuracy and so-called hallucinations at 71% and regulatory and compliance concerns regarding AI outputs at 54%.

Because of that, Datarails noted that a new technology category was emerging, with a third of CFOs looking to implement a "finance operating system", effectively a governed data layer for AI.

At the moment, finance teams use an average of 2.5 general-purpose models, with Microsoft Copilot the clear leader at 93%, followed by ChatGPT at 65% and Claude at 64%.

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Compliance with AI regulations will present an enormous challenge for businesses, according to ISACA CEO Erik Prusch, describing the task as “Sarbanes-Oxley on steroids”.

Introduced in 2002 following the Enron and WorldCom accounting scandals, the Sarbanes-Oxley Act (commonly abbreviated to SOX) is a US federal law governing the reporting and verification of financial information by public companies.

Initial implementation of the necessary internal controls was notoriously costly and took several years to bed in, and compliance remains an annual exercise for US-listed companies.

Prusch, who was CFO of biometric authentication company Identix when the rules came into force, said the challenge of achieving compliance with incoming AI regulations dwarfs that work.

Prusch warned companies will need to do the same level of work five or even ten times faster thanks to the rapid pace of AI development – and all without a settled regulatory framework to guide them.

“It's the equivalent of SOX on steroids,” he told ITPro in an interview at the annual ISACA Europe conference in Munich. “It took us multiple years to get to a place where we actually knew what we were required to do from a Sarbanes-Oxley standpoint, and that was with the benefit of regulation that told us something to do.”

Dazed and confused over AI compliance

Part of the challenge with AI compliance, he explained, is that unlike SOX, the industry is still in the process of establishing the controls, accountability, and reporting lines that compliance will be based on.

“Who is responsible, who is ultimately going to be accountable, and how that is going to get communicated to the board, is brand new,” he said.

Further complicating matters, multiple countries and supranational bodies are butting heads over the best approach to regulating AI.

The EU's more cautious approach has favoured a comprehensive, risk-based rulebook – laid out in its AI Act – while the US federal government has prioritized development speed and innovation, resisting attempts to impose regulation at home and pressuring Brussels to scale back its own rules.

Combined with NIS2, DORA, and the Cyber Resilience Act, which govern cybersecurity for everything from critical infrastructure and banks to connected products, the AI Act makes the EU one of the more demanding regulatory environments for businesses.

Many organizations fall under several of these regimes at once, each with its own overlapping obligations.

"Over the last couple of years, we have seen a definite increase in the compliance burden," said Tara Wisniewski, senior vice president of advocacy at ISACA, citing complaints she’s heard from members.

"My DORA regulation says it has to look this way, NIS2 says it has to look this way, now CRA wants it this way… Why do I have to provide the same piece of information in six different colours? It gets ridiculous."

No light on the horizon

Prusch warned that the situation is likely to get worse before it gets better. He predicted that board members will be named in lawsuits when AI deployments go wrong, arguing that, as with SOX, regulation will arrive as a reaction to a string of high-profile failures.

"It's only going to take a couple of companies that frankly go out of business because they're exposed, and they haven't done the work necessary," he said. “Everybody’s going to have that ‘oh shit’ moment.”

"We're probably 12 months from panic," he added.

As part of efforts to address the gap, Wisniewski said ISACA is expanding its advocacy work, engaging with policymakers on the principles that should underpin AI regulation, and calling for greater consistency between different jurisdictions' rules.

This includes working with UK lawmakers on amendments to the Cyber Security and Resilience Bill.

At the same time, the association has also launched three AI-focused certification products, with a fourth on the way, and is building AI into established credentials such as CISA and CISM. Until regulation catches up, however, Prusch warned the onus is on businesses themselves.

“Every organization that is utilising AI without proper controls, without understanding risk, without governance policy principles, without cybersecurity awareness, is exposing that company to a traumatic amount of risk,” Prusch said.

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HPE has launched a range of new servers optimized for enterprise inference and agentic AI.

The firm said the HPE ProLiant Gen13 Servers, powered by sixth-generation AMD Epyc Server CPUs, deliver higher core density, memory bandwidth, and I/O.

These servers are primarily targeted at compute-intensive workloads such as agentic AI and inference, analytics, virtualization, simulation, and modeling, the company said in an announcement.

“Organizations need to turn their investments in agentic AI and inference into business value without adding operational complexity or risk,” said Krista Satterthwaite, senior vice president and general manager for compute at HPE.

“For these workloads, HPE ProLiant Gen13 brings together optimized performance, built-in, automatic security, intelligent operations, and proven reliability. It gives customers a trusted foundation to modernize and innovate today and adapt as their requirements evolve.”

Under the hood of HPE's ProLiant Gen13 servers

The HPE ProLiant DL585a Gen13 Server is a high-density platform designed to support up to eight double-wide PCIe GPUs, two sixth-generation AMD Epyc Server processors, and PCIe Gen6 connectivity.

Conversely, the HPE ProLiant DL525 Gen13 Server is a more compact, performance-optimized 1U, single-socket system powered by one sixth-generation AMD EPYC Server processor with up to 256 cores.

HPE said this features high bandwidth 16-channel memory, with support for high-speed and high performance –DDR5 and MRDIMM – and PCIe Gen6 connectivity.

Notably, this is suited more for workloads with extremely high data throughput, such as fraud detection, market-data processing, risk and pricing models, electronic design automation (EDA), simulation, and AI inference.

Meanwhile, the HPE ProLiant XD245 Gen13 and HPE ProLiant XD285 Gen13 are designed to maximize compute performance within a smaller footprint. The two OCP-compliant servers combine high-density compute with efficient shared power and cooling to support data-intensive AI and HPC workloads.

Finally, the HPE ProLiant XD245 Gen13 Server uses a one-Open Rack Unit (1OU), four-node direct liquid-cooled design, and is powered by up to eight sixth-generation AMD EPYC Server processors; while the HPE ProLiant XD285 Gen13 Server uses a two-Open Rack Unit (2OU), two-node air-cooled design.

More on the horizon for HPE

According to HPE, there are more systems planned in the near future. The new servers combine automatic security from HPE Integrated Lights-Out 8 with enhanced HPE Compute Ops Management capabilities to protect data and system integrity.

The company will also introduce new features, including automatic self-encrypting drive (Auto-SED) server capabilities to protect data at rest from first boot without added configuration, external key managers, or manual activation.

Multi-person authorization (MPA) requires two authorized users to approve high-impact actions, including key provisioning and secure erase, while expanded post-quantum cryptography (PQC) readiness now extends beyond firmware to help protect server communication paths from emerging threats - a first in the industry, HPE said.

“AI is moving into the applications and operations that run the enterprise, fundamentally changing what organizations demand from their infrastructure,” said Dan McNamara, senior vice president and general manager, Compute and Enterprise AI, AMD.

“Across generations of AMD Epyc CPUs and HPE ProLiant servers, our teams have worked side by side to turn advances in processor and system design into practical value for customers,” McNamara added.

“With sixth-generation AMD EPYC processors and HPE ProLiant Gen13, we are delivering the performance, efficiency and flexibility enterprises need to scale AI while modernizing the critical workloads their businesses depend on.”

The HPE ProLiant DL525 Gen13 server will be available later in 2026, with the HPE ProLiant DL585a Gen13 server rolling out in early 2027 and the HPE ProLiant XD245 and XD285 Gen13 servers later that year.

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Updates to the Dell AI Data Platform aim to provide more reliable AI systems and reduce costs, according to the tech giant.

In a sweeping update to the platform, Dell technologies has unveiled three new features, including a “Unified Semantic Layer” designed to improve visibility for unstructured and structured data sources.

In a blog post detailing the move, the company said this will give data more “consistent business meaning” by providing users with rules, definitions, and a searchable glossary “so a term means the same thing everywhere it appears”.

“If one system calls it a client and another calls it an account, AI now knows they’re the same thing,” Dell explained. “Users can also import existing ontologies and classification taxonomies, reusing industry-standard or enterprise-specific taxonomy assets”.

As part of this, Dell confirmed it will draw upon Nvidia Auto-Ontology, an open source library used to design and build knowledge graphs.

Dell targets AI context gains

On that front, platform users will now have access to the Enterprise Knowledge Graph feature. According to Dell, this maps how structured and unstructured data is related, and plays a key role in the Unified Semantic Layer.

“It uses metadata, lineage, and query history to keep tuning the graph as activity changes. When an agent asks a question, the platform pulls in every related piece it's allowed to see,” the company noted in a blog post.

New "Knowledge Agents" will run parallel to these new features, according to Dell Technologies.

The company said these “put context to work” and will act as a complementary tool to provide teams with insights on data sources and infrastructure environments.

AI costs in the spotlight

Dell Technologies said the new features come at a pivotal time for organizations, many of whom are scaling AI projects and encountering infrastructure-related bottlenecks.

“In most cases, the model isn’t the bottleneck to achieving enterprise scale for AI,” the company said. “The data is. A company’s most useful information sits in files, databases, cloud services, and systems across multiple sites.”

These disparate and fragmented data sources mean agents burn through tokens to reconstruct answers and derive context from enterprise information, said Arthur Lewis, president of Dell’s Infrastructure Solutions Group.

“Data without context is just noise,” he commented. “Most enterprises have spent years making their data accessible. That’s not the same as making it usable.”

“An agent that can find a customer record but doesn’t know what it means, how it connects to everything else, or whether it can be trusted isn’t intelligent. It’s just fast.”

Lewis added that by focusing on providing greater visibility and context for agents will “get more out of the data they already have”.

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Tesco has announced the appointment of Chris Blatchford as chief technology officer (CTO) and a member of the executive committee from March 2027.

He will replace the supermarket giant’s current CTO Guus Dekkers, who is retiring in December following eight years with the company.

Blatchford joins Tesco from Kingfisher plc, where he currently serves as group CTO and is responsible for technology, data, and product platforms across its B&Q, Screwfix, Castorama, and Brico Dépôt businesses.

In an announcement, Tesco highlighted Blatchford’s track record of driving digital transformation, modernizing technology platforms, and applying data and emerging technologies to improve experiences for both customers and staff.

Blatchford said he is excited to be joining the business at a time when the technology agenda is more important than ever.

“There are so many opportunities for technology to improve the experience for customers and colleagues across the Tesco ecosystem,” he said.

“Tesco has built an outstanding technology organisation under Guus's leadership, and I'm looking forward to working with the team to build on that momentum and continue delivering technology that makes a real difference for customers, colleagues and the business."

Retiring CTO Guus Dekkers joined Tesco in 2018, later becoming a member of the executive committee in March 2021, and his tenure saw technology become an increasingly important factor in the company’s success.

He is credited with playing a pivotal role in the modernization of Tesco's technology capabilities, the strengthening of its digital and data foundations, and the building of growth-enabling platforms.

"Leading the Technology team at Tesco has been a huge privilege,” Dekkers commented. “I know the team will continue to go from strength to strength under Chris's leadership, and I wish him every success in the role.”

Maintaining leadership continuity

To bridge the gap between Dekkers’ retirement and Blatchford’s arrival, Tesco’s technology director Jane Mustoe will become acting chief technology officer from the beginning of November.

Commenting on the leadership change, Tesco group chief executive Ken Murphy praised Dekkers’ “outstanding contribution” to the business’ success.

“His leadership has been central to strengthening our technology capabilities and delivering our strategic ambitions,” he said.

“I'm also grateful for the contribution he has made as a member of our Executive Committee and his commitment to developing technology talent."

Murphy added that the group has found a strong successor in Blatchford as it seeks to continue leveraging technology as part of its ongoing growth ambitions.

“He has led technology, data and digital transformation at scale and brings a combination of strategic vision, deep technical expertise and a strong track record of building high-performing teams,” he said.

“I am confident he will help us continue to strengthen the role technology plays in serving customers and colleagues and supporting our long-term growth."

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The FBI has reportedly removed an Accenture contractor in connection with the recent data breach that exposed sensitive information on thousands of employees.

Data stolen in the incident is believed to have included descriptions of counterintelligence workers' roles, the home addresses of human intelligence operatives, and even the medical and psychiatric records of bureau employees.

The breach, revealed last month, involved a compromise of the FBI’s jobs portal. It was claimed by ShinyHunters, which said it had exploited a vulnerability in Oracle PeopleSoft, an enterprise resource planning (ERP) suite used in human resources (HR), finance, and supply chain management.

FBI cyber chief Brett Leatherman told Reuters the contractor failed to apply the appropriate patch.

“To date, our review has determined that the incident occurred as the result of a security failure ​of a platform managed by a third-party organization — after a contractor failed to implement a security patch explicitly issued to secure the ​platform," he said.

"As such, the FBI has removed the contractor and taken all necessary steps to both mitigate any further risk and protect our workforce.”

Responsibility lies with the organization

It's not clear why the contractor failed to apply the patch. While the removal of the contractor implies it was negligence, David Neeson, SOC deputy team lead at Barrier Networks, suggested that there might have been a good reason, such as a possible impact on another critical system.

"Leadership must always be accountable for security decisions, so if the patch not being applied was out of the contractor’s control, then the FBI could be making a dangerous mistake with their removal. It could also spur other organizations to take similar action when they are trying to shift responsibility around the cause of an attack," he said.

"Patch management is never simple, and the FBI must clearly understand this.”

This view is echoed by Jason Brown, director of customer advisory and counter fraud lead at iCounter, who said that accountability should always reside with the organization itself.

"Every contract for a managed platform should spell out how quickly critical patches have to be applied, require the third party to prove it was done, and give the customer the right to check," he said.

"Security teams also need to stop treating third-party managed systems as someone else's problem. Keep an inventory of every platform a third party runs on your behalf, know what data sits in it, and watch for warning signs, like that platform showing up in exploit activity or criminal forums."

Extortion group ShinyHunters has been operating since 2019, with members apparently coming from all over the world. Since that time, the group has been associated with a number of major breaches, including the European Commission, Snowflake, LastPass, Okta, AMD, and Salesforce.

International law enforcement bodies have been working to take down ShinyHunters for some time, most recently with the arrest of a 24-year-old Dutch man last month and another man in Jordan this week. Detainees are reported to be cooperating with the FBI.

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AI and software spending is skyrocketing in Europe, and new research warns organizations are failing to curb costs by rationalizing their software bills.

According to analysis from Spendesk, AI spending has risen by 340% in the last year, but finance teams are failing to keep on top of increasingly fragmented and unpredictable spending.

Companies are spending an average of around £213,000 per year on software, while 72% of businesses using AI are paying for two or more competing tools.

All told, Spendesk calculates that almost £6 billion could be saved across Europe by tackling duplicate software.

UK small businesses alone could save almost £399 million by rationalizing their software spend, the company claimed.

Spending volatility

AI-related spending is also highly volatile, according to Spendesk, with six-in-ten of the companies analyzed showing significant month-to-month variations. Bills varied by up to 61% from one month to the next, with peak months for spending costing up to eight times more than low-spending months.

This, the company noted, makes budget forecasting far more difficult and could land some businesses with huge, unexpected bills.

Spending with certain suppliers is growing rapidly, with Anthropic increasing sevenfold in six months, while spending related to Cursor more than doubled.

"We’re entering a new phase of AI in the enterprise. We’ve moved beyond using AI to produce and analyse information; we’re now taking it a step further by entrusting it with part of the work itself," said Axel Demazy, CEO of Spendesk.

“For finance teams, that means moving from controlling every single flow to letting AI take on the tasks it can handle, while flagging only those that genuinely require human intervention."

The perils of software duplication

A key concern for businesses lies in software duplication, according to Spendesk, and it’s often a result of neglect. Average license utilization stands at just 49%, meaning that around half the seats a typical company pays for go unused.

Meanwhile, the same software sold to companies the same size, doesn't cost the same amount twice. Researchers described this as the “scale-up danger zone”, noting that headcount often grows faster than procurement discipline does.

"A company can run 30-plus SaaS tools before it has anyone whose job is negotiating the rate on a single one of them."

Spendesk said that dealing with this requires benchmarking. Indeed, SaaS vendors are notoriously opaque about pricing, and finance teams can't negotiate when they don't know what other companies are paying.

Spend efficiency is also a blind spot, with cost-per-output becoming the metric that separates companies scaling AI profitably from companies just scaling AI spend.

Those that treat AI spend as a strategic input, not a line-item afterthought, will spend the next twelve months pulling ahead of the competition, researchers noted.

"The next step is to connect software and AI spend to business outcomes: the time saved, output generated, customers supported or revenue enabled," advised the firm.

"A tool may be worth keeping even if it overlaps with another, provided its role is clear and its value can be measured. The question is not simply 'what can we cut?' but 'what is this spend helping the business achieve?'”

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