How should teams assess IAM maturity when NHIs and AI systems are in scope?

Why This Matters for Security Teams

IAM maturity looks different once NHIs and AI systems are in scope because the identity is no longer a person with predictable working hours and stable access patterns. Service accounts, API keys, workload identities, and autonomous agents can be created faster than teams can review them, and they often accumulate privileges that never map cleanly to a human role. NHI Management Group notes that only 5.7% of organisations have full visibility into their service accounts in the Ultimate Guide to NHIs, which is a clear sign that maturity cannot be judged by human IAM controls alone.

The practical issue is not whether there is a policy on paper, but whether lifecycle, access, and offboarding processes can keep pace with identity behaviour that is dynamic, ephemeral, and sometimes autonomous. Standards such as the OWASP Non-Human Identity Top 10 and NHI guidance from NHIMG both point to the same gap: organisations frequently treat machine access as an exception instead of a first-class identity class. In practice, many security teams encounter the maturity problem only after secrets sprawl, overprivileged workloads, or an agent-led tool chain has already expanded access beyond what the original review covered.

How It Works in Practice

A useful maturity assessment starts by separating identity types and then scoring the controls that actually govern them. Human IAM metrics, such as SSO coverage and MFA adoption, are still relevant, but they do not prove readiness for workloads or agents. For NHIs, the core maturity questions are whether the organisation can discover every identity, bind it to an owner, issue least-privilege access, rotate or revoke credentials quickly, and detect when a secret is used outside its expected context. For AI systems, the bar rises again because the identity can make decisions, chain tools, and request new access at runtime.

Current guidance suggests evaluating maturity across four operational layers:

• Discovery: can the team inventory service accounts, API keys, workload identities, and agent tool credentials continuously?

• Lifecycle: are secrets issued just in time, time-bound, and revoked automatically when the task ends?

• Authorization: is access checked at request time using context, rather than relying only on static RBAC assignments?

• Governance: can the team prove ownership, business justification, and periodic review for each non-human identity?

For AI-heavy environments, runtime policy becomes more important than pre-defined access bundles. The NIST AI Risk Management Framework is useful here because it frames governance, mapping, and measurement as ongoing activities, not one-time approvals. Similarly, the 2024 Non-Human Identity Security Report shows that 88.5% of organisations say their NHI IAM lags human IAM, which suggests maturity should be measured by operational consistency, not policy completeness. These controls tend to break down in multi-cloud and agentic pipelines because identity sprawl, ephemeral execution, and cross-system tool chaining make static reviews obsolete before they are finished.

Common Variations and Edge Cases

Tighter identity control often increases operational overhead, so organisations have to balance stronger assurance against slower delivery and more frequent exceptions. That tradeoff becomes most visible in environments with high deployment velocity, hybrid cloud, or AI agents that need short-lived access to multiple tools in sequence. In those settings, a rigid approval workflow can create shadow access, while a loose workflow creates untracked privilege expansion.

Best practice is evolving, but there is no universal standard for how to score “maturity” for autonomous agents yet. Some teams use separate maturity tracks for humans, NHIs, and AI systems; others use one model with different control weights. The important point is that the same control can mean different things in each category. For example, credential rotation is a hygiene measure for service accounts, but for an AI agent it may need to be tied to task completion, runtime policy, and workload attestation. The most reliable references for this emerging area are the Top 10 NHI Issues and the OWASP guidance on non-human identity abuse paths. Organisations should be cautious about treating “covered by PAM” or “covered by RBAC” as proof of maturity, because those labels often hide unmanaged secrets, stale service accounts, and agent permissions that were never reviewed for autonomous use.

Related resources from NHI Mgmt Group

• How does the rise of AI identities impact traditional IAM systems?
• How should security teams limit the risk from AI agents that have access to production systems?
• How should security teams govern AI agents that can access enterprise systems?
• What should teams do when AI agents or NHIs create access patterns that provisioning did not anticipate?