IAM teams should expand ownership, review, and offboarding processes so they apply to AI services and supporting non-human identities, not only human users. AI introduces assets that can be provisioned quickly, used broadly, and left behind without a clear leaver event. Lifecycle governance has to follow that pattern.
Why This Matters for Security Teams
Once AI enters the environment, IAM stops being only a human lifecycle problem. AI services, agents, and supporting NHIs can be created rapidly, granted broad API access, and left active long after the business need has changed. That shifts the risk from occasional orphaned accounts to persistent machine access that is hard to inventory, harder to review, and easiest to overlook during normal joiner-mover-leaver processes. NIST’s NIST Cybersecurity Framework 2.0 is helpful here because it pushes organisations to treat identity governance as an ongoing control function, not a one-time setup.
The practical issue is that AI workloads often require credentials, tokens, and service permissions that do not map cleanly to employee-centric IAM tooling. That is exactly where lifecycle ownership breaks down: the people who approve the model or automation often are not the people who decommission its access later. NHIMG research shows that 88.5% of organisations say their non-human IAM practices lag behind or merely match their human IAM efforts, which is a strong sign that process design has not caught up with workload reality. In practice, many security teams encounter AI-related privilege sprawl only after a service account, token, or secret has already been reused beyond its intended scope.
How It Works in Practice
The IAM change is not just adding AI as another asset class. It is redesigning how access is granted, reviewed, and revoked for autonomous or semi-autonomous workloads. Current guidance suggests that AI services should be governed as NHIs with explicit ownership, short-lived credentials, and policy decisions made at runtime wherever possible. That means moving away from static role assignments that assume predictable access patterns and toward context-aware authorisation that evaluates what the workload is trying to do, where it is running, and whether the action matches approved intent.
In practice, teams usually need four shifts:
- Assign a human owner to every AI service, agent, model endpoint, and orchestration account.
- Issue just-in-time credentials or tokens per task, with short TTLs and automatic revocation on completion.
- Prefer workload identity over shared secrets, so the system proves what it is rather than relying only on a stored password or API key.
- Review access on the same schedule as the model or agent changes, not only on employee transfer or termination events.
This is why frameworks such as NIST Cybersecurity Framework 2.0 and current NHI guidance are increasingly paired with runtime policy enforcement. If the workload is an agent, the safer control point is the request itself, not a standing entitlement approved months earlier. NHIMG’s The State of Secrets in AppSec report is relevant because secrets remain one of the main ways AI systems get broad, durable access without meaningful expiry or oversight. These controls tend to break down when AI tooling is embedded in fast-moving platform engineering environments because access is created through automation faster than governance workflows can classify it.
Common Variations and Edge Cases
Tighter AI access governance often increases operational overhead, requiring organisations to balance security gain against developer velocity and automation reliability. That tradeoff is real, especially when teams are supporting experimentation, model testing, and multi-environment deployments at once. Best practice is evolving, but there is no universal standard for how granular AI identity should be across every use case.
Some environments can still use RBAC as a coarse baseline, especially for low-risk read-only workloads, but RBAC alone is usually too static for agents that chain tools or change behaviour based on prompts and context. Where there is higher privilege, current guidance favours ephemeral credentials, workload identity, and policy-as-code with runtime evaluation. For hybrid and multi-cloud estates, this becomes even more important because NHIMG research shows that 35.6% of organisations cite consistent access across environments as their top NHI challenge. The edge case to watch is shared orchestration platforms: if one control plane issues long-lived access to many AI services, a single compromise can multiply into a broad identity failure. AI governance is weakest when teams assume the model layer is separate from IAM, because in practice the model often becomes the most aggressive consumer of 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 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A01 | Static IAM fails for agents with unpredictable tool use and runtime actions. |
| CSA MAESTRO | MAESTRO-2 | Covers identity, trust, and policy controls for autonomous AI systems. |
| NIST AI RMF | AI RMF addresses governance and accountability for AI-enabled access decisions. |
Document ownership, monitor access behavior, and review AI risks throughout the lifecycle.
