Autonomous AI agents challenge a core assumption of your IAM program: that software behaves as its code dictates. This checklist helps IAM architects determine which workloads have moved from standard non-human identity (NHI) management to agent identity territory, and outlines the necessary controls. Each item has a clear done/not-done state and a description of what success looks like.
Prerequisites
Before you begin, ensure you have:
- An inventory of NHIs: service principals, API keys, OAuth 2.0 client registrations, and workload identities. Machine identities often outnumber human ones by 100:1 or more in dense containerized and serverless environments, so expect a large inventory.
- A list of every AI or LLM-driven workload in production or pilot, including agent frameworks owned by product teams outside IAM's purview.
- Read access to your SIEM, authorization policy store, and tool or API registries.
Checklist Items
1. Apply the three-part threshold test to every AI-driven workload.
Evaluate each workload with three questions. Does it use non-deterministic reasoning, like an LLM loop, to choose actions? Does it dynamically select tools or endpoints at runtime? Does it act on behalf of a human with multi-hop delegation to other agents or platforms? If all three apply, it needs a dedicated agent identity model.
Done: Every AI workload has a recorded test result.
Good looks like: A classification field in your NHI inventory (standard NHI or agent) reviewed whenever the workload's architecture changes.
2. Reclassify agents currently registered as plain service principals.
Identify agents running under static OAuth client IDs, shared service accounts, or long-lived API keys.
Done: No agent that passes the threshold test remains in the standard NHI population.
Good looks like: Each agent has its own identity type, owner, and lifecycle policy, separate from batch jobs and microservices.
3. Eliminate static, all-purpose permission sets for agents.
A broad permission set can lead to unauthorized data access or destructive operations.
Done: No agent holds a standing role broader than its current task requires.
Good looks like: Privileges are evaluated and bound at the point of action, not granted at startup.
4. Issue short-lived, task-derived credentials.
Extend workload identity federation so tokens are minted per task with scopes derived from the declared task.
Done: No agent uses a long-lived API key or a startup token that outlives its task.
Good looks like: Credential lifetime tracks task duration, measured in seconds or minutes for ephemeral sub-agents.
5. Adopt a composite identity: initiator, agent instance, task context.
Your tokens and logs should answer three questions: which human started this, which agent instance acted, and what task was declared.
Done: Downstream calls carry claims for all three.
Good looks like: An auditor can trace any action to a human principal and a specific agent instance without joining logs by hand.
6. Close both delegation failure modes.
Reject full credential inheritance and identity stripping. Use Delegated Authorization with bounded, narrowed scope instead.
Done: You've tested one delegated flow end to end and confirmed the agent's effective permissions are a strict subset of the user's.
Good looks like: Delegation tokens are narrower than the user's own authority and preserve the initiator's identity.
7. Verify workload identity is not your only control.
Workload identity proves which software is running but not that a given action should be allowed.
Done: Every agent action passes a runtime authorization decision, not just an authentication check.
Good looks like: A Policy Decision Point evaluates each tool invocation against initiator, agent instance, and task context.
8. Automate ephemeral sub-agent lifecycle.
Provisioning pipelines must keep up with agents that live for seconds.
Done: Sub-agent identities are created and revoked programmatically with no manual step.
Good looks like: No orphaned agent identities remain after task completion.
9. Preserve non-repudiation in telemetry.
Done: Your SIEM ingests agent events with the human initiator, agent instance, and task identifiers intact.
Good looks like: Detection rules can alert on anomalous tool selection by a specific agent on behalf of a specific user.
10. Map your controls to emerging agent standards.
Review the OWASP GenAI Security Project's Agent Control Standard (ACS) against your design.
Done: Gaps between your controls and the published guidance are documented.
Good looks like: A tracked backlog with owners.
Common Mistakes
- Treating the agent as "just another microservice." This approach often leads to failures.
- Over-scoping to avoid friction. Blanket roles guarantee over-privilege. Aim for task-scoped access instead.
- Logging the agent but not the human. Without the initiator's claims, your SIEM can't attribute actions.
- Classifying once. Re-run the threshold test on architecture changes.
- Relying on rotation as the fix. Faster rotation of a static credential is still a static credential.
Next Steps
Start with item 1 this sprint; it costs little and reveals the gap size. Then pick one high-risk agent, one that writes data or touches financial or role-management APIs, and take it through items 3 to 6 as a pilot. Use the result to define a reusable agent identity pattern for your platform teams. The real decision isn't whether to support autonomous agents. It's whether you govern them with an identity plane built for agency or let excessive agency define your threat model for you.
