Yes, but the review points differ because AI agents can change behaviour during execution. Ownership, inventory, entitlement review, and offboarding still apply, yet they must be driven by observed runtime activity rather than static assignment alone. The lifecycle has to keep pace with the agent’s operating scope.
How AI Agent Lifecycle Controls Differ from Other Non-Human Identities
Organisations can reuse the same lifecycle control families, but they should not treat AI agents as static service identities. A workload or service account usually changes little after provisioning, while an AI agent may alter its tool use, delegation pattern, and effective scope over time. That means the control objective stays the same, but the evidence required to support it becomes more dynamic.
For that reason, ownership, inventory, entitlement review, credential retirement, and offboarding still matter. The difference is that the review basis must reflect agent identity as it is actually operating, not just how it was originally assigned. Static lifecycle records are necessary, but they are not enough when runtime behaviour can expand or narrow the real blast radius.
That distinction is especially important for agents that act on behalf of users or call external tools. In those cases, lifecycle governance has to capture delegation, approved actions, and any standing access the agent can exercise, because the review question is not only “who owns it?” but also “what can it do today?” NHIMG’s AI Agent Authorisation Guide is useful here because it ties lifecycle thinking to least privilege and per-action approval.
What Should Stay the Same Across AI Agents and Other Non-Human Identities?
The core lifecycle disciplines do not change: every non-human identity should have a named owner, a current inventory record, a defined purpose, a review cadence, and a retirement path. If an organisation cannot answer those basics quickly, it has a governance problem before it has a technical one. This is true whether the subject is a service account, an automation bot, or an AI agent.
Where teams often overcomplicate the issue is by splitting AI agents into a separate governance island. That usually creates duplicate processes and blind spots. A better approach is to keep one lifecycle model for all non-human identities, then add agent-specific fields where the behaviour can vary, such as active tools, delegated scopes, human approval requirements, and observed action patterns. That keeps the control structure consistent while preserving the distinctions that matter operationally.
Internal resources such as the Ultimate Guide to NHIs and Human vs Non-Human Identity help frame that common lifecycle baseline. They are most useful when teams need to separate the identity object itself from the way the identity is used in practice.
Why Runtime Behaviour Changes the Review Model for AI Agents
The key difference is that an AI agent can be provisioned once and still behave differently tomorrow. It may request a new tool, gain a broader working context, or start performing actions beyond the original intent of its owner. So entitlement review cannot stop at the creation record or the last approved role assignment. It has to incorporate observed activity, current delegation, and any drift in effective privilege.
This is why runtime telemetry matters as much as joiner-mover-leaver logic. If the agent has been acting with a broader scope than expected, the lifecycle record is stale even if the account is technically still “owned” and “active.” NHIMG’s AI Agent Observability, Audit and Incident Response Guide is a good companion reference because it focuses on action attribution, logging, and kill-switch decisions when the agent’s behaviour changes.
Teams should also think carefully about offboarding. For ordinary non-human identities, offboarding often means disabling credentials and cleaning up dependencies. For an AI agent, offboarding may also require revoking delegated grants, removing tool access, invalidating cached context, and checking whether downstream automations inherited its permissions. The lifecycle ends only when the agent can no longer act.
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 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 — Improper Offboarding | AI agents and other NHIs both need safe retirement and access removal. |
| NHI-05 — Overprivileged NHI | Lifecycle review must catch excess standing access as agent scope changes. | |
| NHI-10 — Human Use of NHI | Agents acting on behalf of users create lifecycle and ownership ambiguity. | |
| Recommendation — Revoke agent credentials, grants, and dependencies when the identity is retired. Trim agent permissions to the minimum current working scope. Separate human delegation from the non-human identity’s own lifecycle records. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent behaviour can expand effective privilege during execution. |
| Recommendation — Continuously recheck agent authority against live actions. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Lifecycle control includes issuing, rotating, and revoking agent credentials. |
| Recommendation — Track and retire agent authenticators on a defined schedule. | ||
Practitioner Guidance
What to prioritise: Use one non-human identity lifecycle process, but add agent-specific review fields for current tool access, delegated authority, and observed runtime scope. That gives you consistency without flattening important behavioural differences.
What to verify: Before trusting a lifecycle review, confirm that the inventory record matches current usage, not just original provisioning. If the agent’s real activity is broader than its assigned purpose, treat that as a control failure, not a documentation issue.
Decision rule: If access can change during execution, base recertification and offboarding on live behaviour plus ownership, not ownership alone. If you cannot observe runtime scope, shorten review intervals and narrow standing access until you can.
Practitioner takeaway: The right model is shared lifecycle governance with agent-aware evidence, because the control family is the same, but the thing being governed is more dynamic.
Related resources from NHI Mgmt Group
- How should organisations govern SCIM for AI agents and other non-human identities?
- When should organisations re-evaluate identity controls for AI agents and non-human identities?
- Why do organisations struggle to secure AI agents and other non human identities in day to day operations?
- Why do AI agents and other non-human identities create more risk when organisations rely on standing privilege?