Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

At the World Economic Forum cyber meeting in Geneva recently, I had an interesting conversation with Vinh Nguyen, who is a strategic security advisor and Senior Fellow for AI at CFR. I wanted to know from him how he sees runtime governance in agentic AI working out practically and what approaches actually work. One of the challenges he mentioned was that yes, we need runtime governance to provide continuous and real time assurance that agents are doing what they are supposed to be doing.

Your last pentest probably took 2 weeks, cost 5 figures, and tested a fraction of your actual attack surface. Meanwhile, your team shipped 47 deployments in the same window, with each one almost completely untested for security. That gap between how fast you ship and how slowly you test is exactly where autonomous AI agents for penetration testing come in, especially with hackers getting smarter and faster each day (They are not using AI to summarize PDFs!).

Security teams face a new imperative: act fast, or risk losing the vulnerability battle. The average enterprise faces thousands of vulnerabilities across a sprawling hybrid attack surface. Adversaries are using AI to discover and exploit weaknesses independently, at machine speed, making traditional disclosure timelines increasingly irrelevant. Scan-and-ticket workflows weren't built for this reality, and neither are the teams asked to execute them with finite headcount and growing board-level scrutiny.

Agentic AI is rewriting the rules of enterprise risk, operating across distributed systems at a speed and scale that most security architectures weren’t designed to handle.

Mythos doesn’t need to be treated as the biggest and baddest in the room. Don’t get me wrong. Depending on the benchmark you’re evaluating against, Mythos is among the top models available today, and generally the best at reasoning. But it’s not leaps and bounds ahead of the race. And when it comes to practical use cases, throwing a general model, even a cutting-edge frontier model, at a problem doesn’t get the best results. Nor is it scalable or cost-effective.

In my last post, I explained the math behind cosine similarity. Cosine similarity is a powerful search technique. When you are dealing with thousands or millions of chunks, it provides a fast, scalable way to find content conceptually similar to the user’s question. That is a major breakthrough. Without vector search, modern RAG would be much harder to build. But the mistake is pushing every retrieval problem into vector search. That is where practical retrieval starts breaking down.