Taking the myths out of Mythos - the role for the channel around AI and security

In this year’s Verizon Data Breach Investigations Report, exploiting vulnerabilities is the most common route that leads to data breaches, at 31 percent of the 22,000 confirmed attacks.

Looking at the most serious software issues that are Known Exploited Vulnerabilities tracked by the Cybersecurity Infrastructure and Security Agency, patching those critical vulnerabilities is down from last year as well. Only 26 percent of CISA KEV issues were fully remediated during 2025, compared to 38 percent in 2024.

Alongside this, Anthropic’s Mythos brings the prospect of even more vulnerabilities being discovered and exploits being created. While Mythos itself is still off-limits for the vast majority of companies, the advent of frontier AI will mean that security teams have more issues to deal with and less time to get their tasks done.

Customers will need help to understand the practical steps that they can take around security in the future. Cutting through the hype around AI and keeping things focused on real-world processes will go a long way.

The current picture for security

Traditionally, customers need security help around product selection, deployment practices and managing those activities over time. Managed Detection and Response (MDR) for endpoint security has been the bedrock of many partners’ approaches, followed by more specialist products and incident response where there are opportunities.

However, what the advent of AI shows us is that getting the basics around security and IT operations is still a challenge. Areas like IT asset management and maintaining an accurate inventory are still not solved, particularly as companies grow in size. While security programmes advocate for complete insight into what is on the network, the reality is that getting to even 90 percent accuracy is a significant challenge. This means that there is already a blind spot in security.

As AI gets adopted, many teams will want to improve their existing patching processes and speed up what they currently do. Automation around security and asset management can help in this regard, but the problem is that speeding up a process that does not cover everything already is not going to be enough. Repeating those same processes faster is not the full answer.

So how can channel companies help their customers in practical ways, and without making them feel despair at the situation? The answer here is to focus on practical responses that companies can take to fix these problems.

Patching is hard work

The first element here is that patching has always been hard work. Enterprises normally have different teams managing each element of the IT estate, so getting a consistent and fast process in place around patching or fixing misconfigurations was difficult before AI came in. Today, security teams have to do more than point out the problems; instead, they have to provide remediation guidance and ideally solve those issues in full.

With AI pointing out more problems, teams have to concentrate on potential business impact. In practice, this means understanding what issues exist in systems and what the risks involved are. What makes this different from previous attempts to manage cyber risk quantification is the level of detail involved. This ‘hyper-personalisation’ around specific software assets, systems deployment and exploitability should guide what vulnerabilities exist and what to fix.

The second element to this is how to automate patching. Traditionally, IT teams have been scared to implement automated patching due to experience with bad patches. In response, channel partners can take customers through how to structure automatic deployments using some lessons learned from software deployment at scale. For example, your customers can tier their assets into groups for deployments - the initial tier will be endpoints that get those patches first and are then checked for potential problems or configuration issues. If that tier of machines is deployed successfully, then the next tier of machines can be updated, and so on. This tiered approach can flag issues early.

Alongside deployment tiering, testing the patches themselves using AI can flag potential issues or where more human oversight for deployment is needed. Lastly, not all patches are created equal - your customers might want expert insight and expertise on hand when they have to patch their mission-critical applications that are responsible for revenue. However, a patch to Google Chrome can be automatically deployed at scale because it is less likely to be problematic.

For customers, these changes around patching can be big. They can involve working across departments and team boundaries, which can be problematic. For partners, just being an external provider can help internal teams talk to each other and solve problems. However, the bigger opportunity is around how to make changes at scale.

The sheer volume of patch updates and remediation work that is expected due to AI vulnerability discovery means that it won’t be possible to stick with the same manual processes. Helping your customers understand where they will have to make changes, and where they can take advantage of AI to improve their processes, is a market opportunity.

Getting ahead of issues before they get added to exploit lists like CISA KEV requires understanding the risks involved within customers, and every customer will have their own priorities and risk tolerance. The challenge is how to help them get the right process in place and ride the wave of AI-discovered vulnerabilities, rather than drowning in them.