AI is uncovering vulnerabilities at a scale that will overwhelm legacy defenses. Here is how to build a security organization that is Mythos ready.

Tenable is collaborating closely with Anthropic, OpenAI and other AI leaders as we integrate advanced AI into our Tenable One Exposure Management Platform, accelerating vulnerability research, remediation automation, and proactive cyber defense. In our recent discussions with these frontier AI model providers, one thing has become clear: the models are a game-changer on multiple fronts. They can identify vulnerabilities in open-source code and complex enterprise environments that have eluded human researchers for decades.

However, this breakthrough presents a paradox. While models like Anthropic’s Claude Mythos and OpenAI’s GPT accelerate our ability to defend, they simultaneously upgrade the capabilities of bad actors, allowing them to discover and weaponize flaws at machine speed. They also threaten to bring to light orders of magnitude more vulnerabilities that need to be prioritized and remediated. 

The attack surface has expanded. It’s no longer just about traditional infrastructure, but about the model access controls, identity entitlements, and operational workflows that surround the AI itself. Whether an attack utilizes an AI-discovered zero-day or targets the AI training pipeline directly, the challenge remains the same: you can’t manage what you don’t see, and you can’t defend what you don’t prioritize.

To thrive in the LLM era, here are the five key actions to take today:

1. Establish continuous, deterministic asset discovery

You can’t find vulnerabilities in assets you haven’t discovered. Organizations must implement a foundation of deterministic sensors (scanners, agents, and passive monitors) to maintain a real-time inventory of every digital asset. And with rapid AI adoption across the world's enterprises, it’s essential to have visibility into all your AI inventory, shadow and sanctioned.

Unlike the probabilistic nature of frontier AI, which can be inconsistent, your discovery must be deterministic. You need an auditable record of what is on your network to provide the "ground truth" required for compliance and risk reporting.

2. Move beyond legacy prioritization to ruthless risk filtering

With Mythos-driven discovery, the volume of vulnerability disclosures is expected to grow by orders of magnitude in the near term. Standard tools like CVSS or EPSS, which only measure theoretical severity or probability, will cause your team to drown in noise.

A Mythos-ready program uses machine learning to narrow the "60% critical" flood down to the 1.6% of vulnerabilities that create actual risk. By cross-referencing AI-discovered flaws with attack paths and business criticality, you ensure your team is fixing the holes that actually lead to your crown jewels, including the AI models themselves.

3. Neutralize toxic combinations via attack path analysis

Attackers don't look at vulnerabilities in isolation. They look for a path. They chain together a minor software flaw, a misconfigured cloud bucket, and an excessive identity permission to reach their target. In the AI era, exposure management is about identifying these "toxic combinations" before an adversary does.

The rapid growth of AI infrastructure means new attack paths form every day. And the intersection of poorly-configured AI infrastructure and traditional IT infrastructure creates powerful weaknesses that can be exploited.

Use attack path analysis to visualize how an attacker might use an AI-accelerated exploit to breach your perimeter and move laterally toward your AI training data or inference engines. If you close the path, the vulnerability becomes irrelevant.

4. Implement adversarial exposure validation (AEV)

When the "prompt-to-exploit" window shrinks from weeks to minutes, theoretical security is dead. You must implement Adversarial Exposure Validation (AEV), a continuous loop of automated red teaming.

By regularly challenging your environment against the MITRE ATT&CK framework, you gain evidence of how your defenses hold up against AI-speed exploits. This is the only way to ensure your incident response plan isn't just a document, but a proven shield against the reality of a Mythos-driven breach.

5. Govern AI exposure with agentic remediation

The fastest-growing risk surface in the world is the AI infrastructure itself: models, training pipelines, and autonomous agents with high-level access. These are now high-value targets requiring strict monitoring.

To match the speed of the threat, you must deploy agentic AI engines (like Tenable Hexa AI) to automate the triage and remediation of these exposures. This allows for "machine-speed defense" — using AI to discover, tag, and patch your infrastructure at the same velocity that Mythos is discovering its flaws.

The bottom line

The window to act is narrow. In our active conversations with the Office of the National Cyber Director, the Cloud Security Alliance and Anthropic, the consensus is clear that the lowest common denominator approach to security will no longer suffice. This reinforces the criticality of traditional cyber hygiene practices, while stressing the need to build automation and efficient systems into your program. Hope is not a strategy.

We must use the same principles of exposure management to handle the volume this increased discovery creates. See everything, prioritize ruthlessly, and remediate at machine speed. That is what it means to be Mythos ready.

To learn more about how Tenable can help, please also read Tenable CTO Vlad Korsunsky’s recent post “Claude Mythos: Prepare for your board’s cybersecurity questions about the latest AI model from Anthropic.”