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

The report states that autonomous AI agents can pursue tasks persistently across identities, tools, credentials, and reachable resources, making ordinary lateral movement difficult to distinguish from legitimate execution. It recommends discovering all agents, assigning ownership, mapping complete access chains, comparing access with intended purpose, and continuously right-sizing permissions. RealGround analysis: this reflects AI agent abuse risk because excessive autonomy and access can enable unintended privilege escalation or unauthorized movement; business-logic audits, secure agent design, and continuous red teaming can test and reduce these paths.