IAM for AI agents: A Practical Enterprise Framework
Guide proposes an IAM framework for AI agents, treating each agent as a governed non-human identity with scoped, expiring, auditable access.
The article lays out an enterprise framework for identity and access management of AI agents, arguing conventional IAM expresses configured intent but cannot observe what autonomous agents actually execute. It cites OWASP's Top 10 for LLM Applications excessive agency risk (LLM06) and maps controls to NIST SP 800-53 Rev. 5 AC/AU families and the NIST AI RMF. Recommendations include distinct attributable agent identities, workload identity federation with short-lived credentials, OAuth 2.0 Token Exchange (RFC 8693) for delegation, task-scoped grants, tool allowlisting, data boundaries, and action thresholds requiring human approval. It also enumerates recurring lifecycle failures: absent ownership, long-lived secrets, unbounded delegation, invisible instantiation, and no expiration.
- Static entitlements cannot bound autonomous agent behavior; runtime telemetry must prove actual execution
- Agents need distinct identities, short-lived credentials, task-scoped grants, and tool allowlisting
- Five lifecycle failure modes: absent ownership, long-lived secrets, unbounded delegation, invisible instantiation, no expiration
- References OWASP LLM06 excessive agency, NIST SP 800-53 AC/AU families, and RFC 8693 token exchange
From the original article
9 min read at thehackernews.com
What is IAM for AI agents? AI agents authenticate, invoke tools, and act across enterprise systems with delegated authority. IAM for AI Agents is the identity-control architecture that governs those actors.
thehackernews.com
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