AI Agents Are Rewriting the Rules of Lateral Movement

Security teams have spent decades asking whether an identity has too much access. AI agents raise a harder question: how can we determine which paths an autonomous system can discover, given the access it already has?

A person may try several ways to complete a task. A deterministic application follows the flow its developer wrote. But an AI agent is relentless in its pursuit of done. In May 2026, OpenAI announced that one of its models had disproved a 1946 Erdős conjecture in discrete geometry, largely by working through paths a mathematician would abandon as too tedious.

The same principle applies to cybersecurity. An AI agent can test thousands of actions, abandon failed routes, discover credentials, switch tools, and keep going. That persistence is part of what makes agents useful, but it also changes how we need to think about lateral movement. AI agent risk has two dimensions:

• Access defines the possible blast radius
• Autonomy determines how much an agent can do without a human in the loop

Either dimension can pose a risk on its own, but the combination changes the security model. Agent behavior cannot be reliably predicted, but identity and intent make access governable.

Autonomy Turns Access Into Exploration