The AI agent security gap comes from treating agents like ordinary applications when their behavior is closer to credentialed workers. The existing Grey Haven item lists the uncomfortable questions: what the agent can access, what it can change, how intent is logged, who approves escalation, and what happens when it follows a plausible but wrong path through connected systems.
The Grey Haven read is that security models must match behavior, not category labels. An agent can combine credentials, tools, memory, and judgment across systems. That makes least privilege, approval boundaries, audit trails, test environments, and incident response essential before autonomy touches high-impact workflows.
CISOs, product security teams, platform owners, and founders building agent products should care because the first serious incidents will not look like normal app bugs. They may look like an authorized system doing the wrong thing at scale, leaking context through tools, or changing records through a chain of actions no one reviewed.
Operator guidance is to classify agents by action authority and data sensitivity. Require scoped credentials, human approval for consequential actions, logging of tool calls, and revocation drills. The watch item is whether security review follows the agent's chain of action rather than the vendor's product category. A system that reads, reasons, and writes across tools deserves controls closer to workforce access than a normal feature toggle. For this signal, the near-term operating question is whether the team responsible for the ai agent security gap no one is talking about can translate the market lesson into one governed workflow, one accountable owner, and one measurable decision point. Source and provenance: manifest record 10, published Grey Haven backfill item on the AI agent security gap, with no external source URL in the manifest.
