Yes, but not by creating a completely separate discipline. AI agents are still non-human identities, so the lifecycle, entitlement, and review model should stay consistent while the runtime controls change. The practical difference is that agents need behaviour-aware governance because their access path can shift during a session.
Why This Matters for Security Teams
AI agents are not just another type of service account. They are autonomous, goal-driven workloads that can choose tools, chain actions, and change their access path during a session. That means governance must account for runtime behaviour, not just static ownership and provisioning. The distinction matters because traditional service account controls assume predictable purpose, while agents introduce conditional execution and context-sensitive privilege.
Current guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework points toward behaviour-aware controls, not a separate identity universe. That aligns with NHIMG research on why organisations struggle with NHI oversight in the first place: in The State of Non-Human Identity Security, only 1.5 out of 10 organisations are highly confident in securing NHIs, while 45% cite missing credential rotation as the top cause of NHI-related attacks. In practice, many security teams encounter agent abuse only after the agent has already chained access across tools, rather than through intentional governance design.
How It Works in Practice
The practical approach is to keep the NHI lifecycle model consistent, then add agent-specific runtime controls. Inventory the agent as an NHI, assign ownership, define its purpose, and review its entitlements. Then shift the enforcement point from static role assignment to runtime authorization based on task, context, and policy. That is the direction reflected in CSA MAESTRO agentic AI threat modeling framework and the broader standards discussion around request-time policy evaluation.
For agents, the strongest pattern is usually workload identity plus short-lived credentials. A workload identity proves what the agent is, while ephemeral tokens or secrets prove what it may do right now. This is where models such as SPIFFE, OIDC-backed federation, and policy engines such as OPA or Cedar become relevant. Instead of pre-approving a fixed role, security teams can issue just-in-time access for a narrowly defined task, revoke it on completion, and log every tool call for review. NHIMG’s OWASP NHI Top 10 coverage and the CoPhish OAuth Token Theft via Copilot Studio case both show why static trust assumptions fail once an agent can broker access through multiple systems.
This guidance breaks down in environments that still rely on shared credentials, long-lived API keys, or manual approval chains, because the agent can continue operating after the approved context has changed.
Common Variations and Edge Cases
Tighter runtime control often increases operational overhead, so organisations must balance security gain against latency, engineering complexity, and policy sprawl. There is no universal standard for agent governance yet, which is why current guidance suggests treating the agent as an NHI for lifecycle purposes while using separate controls for execution risk.
One common edge case is a hybrid workload where a service account launches an agent on behalf of a user. In that model, the service account should not inherit broad standing privilege just because the agent may need intermittent access. Another edge case is multi-agent orchestration, where one agent delegates work to another. That pattern increases the need for request-time policy checks and tight secret scoping, especially where one agent can indirectly reach customer data or admin APIs. It is also important not to confuse a human approval step with safe authorisation. A human review at deployment time does not control what an agent will do when it later receives new prompts or external inputs. The better pattern is continuous evaluation, short TTL credentials, and explicit revocation triggers tied to task completion or anomaly detection.
In practice, teams that try to manage agents exactly like service accounts usually discover the gap only after a tool chain, prompt injection, or token theft event has already expanded access.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Agent goal drift and tool abuse require runtime controls beyond static roles. |
| CSA MAESTRO | GOV-3 | MAESTRO maps well to agent lifecycle, ownership, and runtime risk controls. |
| NIST AI RMF | GOVERN | AI RMF governance covers accountability for autonomous agent behaviour. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Short-lived credentials and rotation are central to safe NHI and agent governance. |
| NIST Zero Trust (SP 800-207) | PR.AC-4 | Zero trust supports request-time authorization for unpredictable agent activity. |
Replace standing secrets with ephemeral credentials and enforce rotation or revocation on completion.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on August 25, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
