They should be governed primarily as workload identities, because their access is machine-executed and often ephemeral, but with additional lifecycle controls that account for ownership and intent. Human IAM patterns are too coarse for agent speed and autonomy, while workload controls alone do not address accountability.
Why AI agents fit workload identity governance first
AI agents behave more like workloads than employees because they execute machine-to-machine actions, often on short-lived credentials, and they may operate across systems without a stable human session. That makes the core control problem how the agent is represented, delegated, authenticated, and retired, not how a person logs in. Human IAM still matters, but mainly at the point where a person approves, owns, or constrains the agent.
The practical distinction is that human-user patterns assume a relatively durable account, predictable session behavior, and broad lifecycle workflows. AI agents need controls that tolerate speed, delegation, and programmatic execution, which is why workload identity concepts such as SPIFFE SVIDs and attestation map so well to the machine side of the problem. Treating an agent as a person usually leads to either over-permissioned automation or brittle approval friction.
The right framing is therefore hybrid but asymmetric: the agent should inherit the discipline of workload identity for authentication, trust, and access, while human governance defines ownership, intent, and exception handling. Non-human identity governance is the natural home for that model when the actor is software rather than a staff user.
What changes when the actor is software instead of a person
AI agents can make multiple decisions in a short period, invoke tools, and move between environments with little or no direct human intervention. That means the access model has to be bound to task scope, runtime context, and revocation speed. Per-action authorization and just-in-time privilege are more appropriate than standing entitlements or coarse role buckets.
Human-user governance alone is too blunt because it tends to overemphasize login events and underemphasize what the software is allowed to do after authentication. For agents, the important questions are whether the principal can be uniquely identified, whether its authority is delegated rather than assumed, and whether each action is bounded by policy. Zero-trust patterns for AI agents fit here because they force verification at request time, not just at account creation time.
At the same time, workload identity controls alone are incomplete if they ignore ownership, business intent, and human accountability. An agent can be technically well-authenticated and still operationally unsafe if no one can explain why it exists, who approved it, or when it should be removed. That is why agent identity governance has to include lifecycle controls, not just token issuance and validation.
How to decide between human IAM and workload controls
Use workload identity as the default when the actor is executing code, consuming APIs, or acting through a runtime system that can be attested and bounded. Use human IAM only for the human oversight layer, such as approval, review, escalation, and ownership assignment. The more autonomous the agent becomes, the more its security model converges on machine identity, because the access path is no longer a user sitting behind a browser.
The most reliable operating model separates three things: the human who owns the outcome, the agent identity that executes the task, and the policy that decides what the agent may do. That separation makes audit trails clearer and reduces the temptation to reuse human credentials for automation. Where agents interact with tools or other agents, agent-to-agent authentication and delegation controls become part of the same governance surface.
This also explains why “treat agents like employees” is the wrong default. Employees need broad access continuity and managerial controls; agents need short-lived authority, narrow scope, and revocation that works at machine speed. If the control design cannot survive credential rotation, task completion, or agent shutdown, it is not a good fit for agentic systems.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST SP 800-53 Rev 5, NIST Zero Trust (SP 800-207) and NIST SP 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-04 — Insecure Authentication | AI agents need strong, non-human authentication flows. |
| NHI-05 — Overprivileged NHI | Agent authority must stay narrow and task-bound. | |
| NHI-07 — Long-Lived Secrets | Agent tokens and keys often outlive the task if unmanaged. | |
| Recommendation — Use strong machine authentication and avoid human credential reuse for agents. Apply least privilege and JIT access to agent credentials. Rotate or eliminate long-lived secrets for agent access. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The question centers on how agent authority should be governed. |
| ASI10 — Rogue Agents | Poorly governed agents can operate beyond intended ownership and scope. | |
| Recommendation — Bind each agent action to a distinct identity and policy decision. Constrain agent autonomy with approval, monitoring, and disablement paths. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Service Identification and Authentication | Agents authenticate as services or workloads rather than people. |
| IA-5 — Authenticator Management | Agent tokens, keys, and secrets need lifecycle control. | |
| AC-6 — Least Privilege | Agent access must be narrowly scoped to reduce blast radius. | |
| Recommendation — Treat agent authentication as service-to-service identity and verify each principal. Manage agent credentials with rotation, revocation, and strict issuance controls. Grant only the minimum permissions needed for each agent task. | ||
| NIST Zero Trust (SP 800-207) | Zero Trust Architecture | Agent access should be continuously verified and never assumed. |
| Recommendation — Apply continuous verification and remove standing privilege for agents. | ||
| NIST SP 800-63 | Digital Identity Guidelines | Ownership and assurance matter where humans sponsor agent authority. |
| Recommendation — Tie agent sponsorship, lifecycle, and assurance to documented identity governance. | ||
Practitioner Guidance
What to prioritise: Establish a separate agent identity model before scaling use cases. The first question is not “what can the agent do?” but “what is the smallest authority needed for this specific runtime and how is it revoked?”
What to verify: Make sure every agent has a named human owner, a distinct machine principal, and a documented offboarding path. If you cannot answer who approves the agent, who monitors it, and who can disable it, the control model is not mature enough.
Decision rule: If the agent can call tools, APIs, or downstream systems without a person in the loop, govern it like a workload identity with delegated authority. If a human is directly performing the action, keep it in human IAM.
Common mistake: Reusing human credentials or long-lived shared tokens for convenience. That erases attribution, weakens revocation, and makes agent activity look like human activity in logs.
Practitioner takeaway: The safest model is not “either human or workload”, it is workload identity for execution plus human accountability for intent, ownership, and exception handling.