Chris Morosco, Aurascape VP & Head of Marketing
April 14th, 2026 | 🕐 6 minute read

Introduction

Anthropic’s release of Claude Mythos Preview matters for a simple reason: it turns a vague industry prediction into something concrete. Anthropic says Mythos has already identified thousands of zero-day vulnerabilities across critical software, including major operating systems and browsers, and launched Project Glasswing to put that capability into the hands of defenders first. Whether this is just clever marketing or every claim holds up over time is not even the main point. The point is that a frontier model vendor is moving directly into cybersecurity work that used to belong to a long list of standalone security products and services.  

So, is AI taking over cybersecurity? 

Partly, yes, I think it is. 

Where AI Is Replacing Security Tools

It is likely to take over a large share of offline security work first. Vulnerability discovery, code review, posture analysis, misconfiguration detection, attack-path reasoning, and other research-heavy or analysis-heavy workflows are all obvious candidates. Those categories reward systems that can ingest massive amounts of code, configs, telemetry, and architecture context, then reason across them quickly. That is exactly where leading AI systems are getting stronger. Anthropic is explicit that Mythos’s cyber strength is a result of being a more capable general-purpose model for coding and agentic tasks, not a narrow point solution. That is a warning to every vendor whose real product is little more than workflow wrapped around LLM analysis.  

That is why Mythos is a real threat to parts of the security market. If the leading AI vendors themselves can find vulnerabilities, propose fixes, reason across attack paths, and eventually automate remediation loops, then a lot of standalone offline security tooling gets squeezed. Some vendors will survive if they own differentiated workflow, proprietary enterprise context, or deep operational integration. Many wrappers will not. The AI vendors will keep moving up the stack. Glasswing points in that direction, even if Anthropic is framing it more narrowly today: giving selected defenders early access to Mythos Preview so they can harden critical software and security products first.  

But that is only half of the story, and it is the less important half. 

The More AI Does Security, the More It Must Be Secured

That is the part many people still miss. If AI is scanning code, reviewing cloud posture, triaging alerts, finding vulnerabilities, proposing remediations, or acting through tools, then someone must control what that AI can access, what tools it can call, what data it can read, what systems it can modify, and how much autonomy it gets. Someone must ensure its prompts are not manipulated, its context is not poisoned, its outputs are not unsafe, and its behavior does not drift outside policy in pursuit of a goal. The more AI does cybersecurity, the more important it becomes to secure the AI itself. That is not the same market as AI-powered vulnerability discovery. It is a different problem entirely. 

This is where traditional inline security starts to break down. The last major architectural shift was zero trust, and for the web and SaaS era it was the right one. Castle-and-moat security failed because it trusted the network too broadly. Zero trust fixed that by turning every connection into its own access decision based on identity, risk, and policy. Zscaler later gave that architecture a metaphor for what they do, describing it as an “intelligent switchboard” that determines who can access what. The logic is simple: verify identity, determine destination, assess risk, enforce policy. That was a meaningful advance over perimeter security. But it still assumes the key question is whether the right actor should reach the right destination. In AI, that is no longer enough.

The Problem with Destination-Centric Architecture

It still assumes the key question is whether the right actor should reach the right application or resource. Even when it adds context, the policy logic still orbits around access to a destination. That is where AI starts to break the model. With AI, a sanctioned destination does not imply a safe outcome. A user can access an approved model and still leak sensitive data. An agent can be connected to an approved tool and still use it in an unsafe way. A model can interact with an approved data source and still overstep policy in how it retrieves, reasons, or acts. In AI, the most important question is no longer just where the connection goes. It is what the system is trying to do. 

This is why AI security becomes the next inline control layer. It must operate on intent, behavior, entitlements, tool access, and runtime action. It must inspect the exchange itself, not just the session. It must understand prompts and responses together, not just requests in isolation. It must know whether an agent should be allowed to call a tool, retrieve sensitive data, pass data to another agent, or take an action on behalf of a user. That is a different control problem than classic zero trust, and it is one traditional vendors were not originally built to solve. Their architectures were designed around users, apps, and destinations. AI pushes the control point deeper into the interaction.

The market data points in the same direction. Deloitte’s 2026 AI report says agentic AI usage is poised to rise sharply over the next two years, while only one in five companies has a mature governance model for autonomous agents. This indicates that AI is on pace to become the majority of all enterprise traffic in five years. AI traffic is rising fast, agentic traffic is rising faster, and governance is lagging adoption. If that continues, AI will become a dominant share of what security systems need to mediate. 

That is the real takeaway from Mythos. Mythos is not just a powerful cyber model. It is a signal that AI vendors are moving into security categories that looked defensible only a year ago. It suggests that a meaningful share of offline security work will be pulled toward the AI model itself over time. But it also sharpens the need for a separate market focused on securing AI systems in production. The better the models get, the more urgent that problem becomes. 

Stronger AI does not reduce the need for AI security. It increases it.  

Conclusion

So, is AI taking over cybersecurity? 

For offline security, a large part of it, probably yes. 

For inline security, not in the way people mean it. What is happening there is not simple replacement. It is an architectural transition. Traditional destination-based vendors will try to extend their platforms with AI-specific runtime controls, whether through internal development, acquisition, or integration. But over time, newer architectures built for AI intent, behavior, and action are likely to take that budget. Someone still must secure the AI itself. Someone still must govern what the model or agent is allowed to do once it is connected. 

That is the category that matters next. The old question was whether the right user reached the right application. The next question is whether the AI should be allowed to take the next action at all. 

If AI is going to take on more of the work, enterprises still need a control layer for the AI itself. That is where Aurascape fits: securing AI and agents inline, with visibility and control over prompts, responses, tool access, entitlements, and runtime behavior. 

See how Aurascape helps security teams govern AI in production: Book a Demo.