They should use it to find where identity governance is fragmented, not to produce a vanity score. A useful assessment shows which identity classes are covered, which controls overlap, and where ownership is missing. That makes it easier to prioritise remediation work that improves discovery, review, and revocation across the actual environment.
Why This Matters for Security Teams
An IAM maturity assessment is useful only when it reveals how identity controls actually behave across humans, workloads, service accounts, and non-human identities. That matters because the real risk is usually fragmentation: duplicate entitlements, missing owners, stale secrets, and access paths that no single team can fully see. The NIST Cybersecurity Framework 2.0 is helpful here because it pushes teams to frame identity as a governed capability, not a one-time audit score.
NHI Management Group research on the State of Non-Human Identity Security found that only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, which is a strong signal that maturity gaps are usually operational, not theoretical. A good assessment should show whether discovery, review, and revocation are consistent across the environment, and whether ownership is clear enough to act on findings. In practice, many security teams only learn where identity governance is broken after a review exception, incident, or audit finding exposes it first.
How It Works in Practice
The assessment should start by mapping identity classes and control coverage, then comparing that map against how the organisation actually operates. That means identifying where the same access model is being reused across very different populations, such as employees, contractors, service accounts, API keys, and NHI workloads. A maturity model is most valuable when it separates discovery, lifecycle management, privilege control, monitoring, and revocation into distinct questions rather than collapsing them into a single score.
Teams should use the output to prioritise remediation by business impact and control weakness. For example, a mature-seeming identity program can still fail if secrets are shared in chat, if access reviews exclude machine identities, or if no one owns dormant credentials. The right response is not always a broad IAM replacement. Often it is targeted work: normalising ownership, closing review gaps, enforcing short-lived access, and reducing secret sprawl. The 2024 Non-Human Identity Security Report is useful here because it shows how many organisations already recognise the gap between human and non-human IAM practice.
- Use the assessment to inventory identity types, not just accounts.
- Score control coverage separately for discovery, access review, rotation, and revocation.
- Assign an owner to every identity class and every exception path.
- Prioritise remediation where stale privileges and shared secrets create the highest blast radius.
A useful benchmark is whether the assessment can drive next-quarter work plans, not just governance reporting. These controls tend to break down when identity data is split across multiple clouds, ticketing tools, and platform teams because no single system has a complete view of entitlement drift.
Common Variations and Edge Cases
Tighter maturity scoring often increases assessment overhead, requiring organisations to balance a cleaner metric against the time needed to collect evidence from many systems. That tradeoff is especially visible when teams try to assess humans and NHIs with the same rubric. Current guidance suggests that is usually too blunt, because workload identities often have different creation, rotation, and revocation patterns than human accounts.
Best practice is evolving toward separate maturity views for distinct identity classes, with shared reporting only at the executive layer. This matters for edge cases such as third-party integrations, ephemeral cloud workloads, and emergency access paths. The Azure Key Vault privilege escalation exposure example is a reminder that apparently narrow permission issues can become systemic when secrets management and role design are not assessed together. Teams should also align findings to NIST Cybersecurity Framework 2.0 outcomes so the maturity exercise supports remediation planning rather than creating a standalone scorecard.
For organisations with heavy cloud automation, there is no universal standard for this yet. The practical test is whether the assessment exposes who can create access, who can approve it, who can revoke it, and how quickly that revocation happens when the identity is no longer needed.
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 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Identity discovery and inventory are core to maturity assessments for NHIs. |
| NIST CSF 2.0 | PR.AC-1 | Access control maturity depends on knowing who and what has access. |
| NIST AI RMF | GOVERN | Maturity assessments should define accountability for identity governance decisions. |
Map identity classes to access controls and close gaps in review, approval, and revocation.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on June 24, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
