Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Governing AI agents at enterprise scale requires a fundamental change in how security, risk, and compliance teams think about AI oversight. The generative AI era focused governance on output quality: what the model says, what it produces, and whether the content meets policy standards. ‍ The agentic era demands governance of action and delegated authority: what the AI is allowed to do, what systems it can touch, and how its decisions trace back to human accountability.

AI agent sprawl is the uncontrolled proliferation of AI agents, autonomous assistants, and LLM-powered tools across an organization without centralized tracking or governance. It mirrors historical IT challenges like SaaS sprawl and shadow IT, and it emerges when decentralized business units build or deploy agents independently, without coordinated oversight from security, IT, or risk teams. ‍ The difference is that these agents are active software actors.

AI is quickly becoming one of the most useful tools available to security researchers. Its ability to analyze enormous volumes of data, identify vulnerabilities, reconstruct attacks, connect seemingly unrelated signals, and help defenders respond faster than humans could alone is incredibly beneficial.

The Remediation Agent and Malicious Code Defense are the first two pieces of Evo Agentic AppSec: security that not only surfaces risk, but resolves it and prevents the next ones. This morning, we announced the broadest expansion of the Snyk AI Security Platform to date: discover, remediate, validate, and prevent. A loop with a missing segment is not a loop; it is a gap that an autonomous attacker will occupy. Evo Continuous Offensive Security closes validation and shipped today.

When we started thinking about how to surface AI model risk inside Evo, the obvious answer was to borrow from how we score everything else: find the issue, assign a severity, surface it. Done. The core of the new approach is a real risk score, built the way security teams already reason about risk: Likelihood × Impact. Likelihood comes from Attack Success Rate (ASR), the share of real adversarial attacks that succeed against a model. Impact is how much damage the attacker's goal does when it lands.

When we first introduced Agent Risk Manager, the response was clear: many security teams are actively looking for a way to secure the AI agents already running in their environment and how they can confidently adopt AI across their organization. AI agents now operate inside organizations with real access to email, files and business systems, often with little visibility for the teams responsible for securing them. That’s exactly the problem Agent Risk Manager was built to solve.

If your board asked today how you are governing AI, how would you respond? Not just the policy you wrote, but what is actually happening across the business. Could you answer with evidence? Many CISOs cannot answer with certainty. AI has entered the business faster than anyone could write policy for it, and securing it across all areas now seems to be the CISO’s responsibility.

Your WAF is doing its job. It's blocking SQLi, XSS, and the usual suspects. But here's the problem: it wasn't built for APIs, and it definitely wasn't built for AI agents. APIs now power nearly every digital experience. And AI agents — the automated systems that access your APIs at machine speed, at machine scale — are the fastest-growing source of that traffic.