What CISOs Can Do Today to Be Mythos-Ready

Which moves make the most impact in your AI Security Program.

The “AI Vulnerability Storm”: Building a “Mythos-ready” Security Program by the Cloud Security Alliance has set the tone for AI security in 2026. Dozens of sitting CISOs and cybersecurity leaders – the people who have spent the last decade-plus thinking about how AI is going to transform security – compiled a comprehensive report to clearly state, “here is what changed, here is why your assumptions no longer hold, and here is what to do about it.”

One of their top recommendations?

“Consistently enforce automated security assessments in your development processes, including using LLM-powered agents to find vulnerabilities before the attackers.”

That’s what Novee AI pentesting is built to do. 

Read on to learn why building the stormguards against AI vulnerability discovery is essential, what CISOs can do immediately to strengthen the muscle, and how Novee can help.

Why Get “Mythos-Ready?” Because Large-Scale AI-Discovered Vulnerabilities are the Norm

The report’s premise is that “Mythos-class” capability won’t even be called “Mythos-class” for very long – the autonomous discovery of thousands of critical vulnerabilities and working exploits generated without human guidance is a change that will proliferate until the capabilities currently behind the Mythos walled garden are widely available. Comparable capability is expected in other frontier models within months, and in open-weight models inside a year. The defensive advantage that early-access programs like Project Glasswing confer is, by definition, time-limited.

The goal of a Mythos-ready program, then, is not simply to return to equilibrium. It’s to build the muscle to stay balanced through the waves that follow.

The Shrinking Exploit Window

Time-to-exploit has collapsed from years to, in some cases, minutes. You can no longer assume a patch will be ready in time to remediate. Incident frequency is set to rise. The CVE system may not scale to AI-generated discovery rates, and novel vulnerabilities have no entry in any known-exploited catalog by definition. Meanwhile, central control is fragmenting: as coding agents reach non-developers, employees stand up their own infrastructure, and threat intelligence already lags behind the pace of discovery and exploitation.

What does that mean for security programs? There is a structural asymmetry between attack and defense; the technology to find and exploit vulnerabilities will always move at the same pace as the technology to defend them, but the cost of exploitation is far lower than the cost of defense. Attempting to defend against a frontier model with another frontier model is an arms race that the defenders cannot win – and one that will drain their resources faster than attackers exhaust theirs.

How to Turn Your AI Capabilities Inward

Before building an AI security plan, the CSA report offers a triage step – “10 Questions to Understand Your Security Program State and Influence.” These are the questions every CISO needs to ask themselves before making a decision about how to scaffold their AI security; what vendors to use, which tools to consolidate, which workflows to augment.

Among them:

• What is our actual stance on AI – allowed, tolerated, restricted, or unknown?
• Can employees use agentic coding tools today, and are there guardrails on them?
• Is there a real security gate between a code change and production?
• Is security operational, or primarily advisory?
• Are our crown-jewel assets explicitly tracked and current?

The point is to establish where you actually stand before layering a plan on top of assumptions that may no longer hold.

From there, the report’s single most actionable recommendation is direct: consistently enforce automated security assessments inside your development process, using LLM-powered agents to find vulnerabilities before attackers do. Turned inward, these tools let you find and fix your own weaknesses first. 

We offer an important caveat to that; pointing Claude Code, or any base LLM model, at your codebases and asking it to substitute itself for a robust OffSec security program is not a solution. LLM-powered agents untrained for adversarial exploitation will not be able to identify the most high-impact vulnerabilities in your system, nor reason on how to replicate and remediate them. 

That’s where Novee comes in.

Why We Built Novee for This

We built Novee in line with the same ethos that prompted this report: the only sustainable response to attackers empowered by AI is to stop treating security testing as a quarterly ritual and run it as a continuous operational discipline. 

A more powerful model doesn’t solve that on its own. It’s solved by architecture – a system that discovers the vulnerabilities that actually cause breaches, proves they’re exploitable in your live environment, and delivers remediation tailored to your specific stack. Attack and defense in one continuous loop.

As the hacker, Novee continuously finds the vulnerabilities that matter, like business logic flaws, authorization gaps, and chained attack paths. As the defender, it closes the loop: every finding is proven real before it reaches your team, paired with stack-specific remediation guidance, and automatically retested to confirm the fix held. We built the platform for zero false positives by design, with independent validation agents confirming exploitability rather than handing your team a queue of maybes. That’s the level a frontier model alone can’t reach, made possible by the offensive engine beneath it.

The teams that build on top of that offensive engine now will meet the next wave of the “vulnerability storm” on their own terms.
To see what your attackers already know – and how Novee finds, proves, and helps you close the vulnerabilities that lead to real breaches, continuously – schedule a demo.