Treat non-human identities as a separate control population with their own inventory, ownership, lifecycle, and reporting. Service accounts, API keys, tokens, certificates, and AI agent credentials should not be folded into generic IAM metrics. That separation makes privilege review, rotation, and offboarding measurable and prevents hidden machine access from accumulating outside normal access governance.
Why This Matters for Security Teams
Governing non-human identities alongside human IAM is a common source of blind spots because the controls are similar in name but not in behaviour. Humans log in interactively, while NHIs authenticate through code, pipelines, workloads, and now AI agents. That difference changes ownership, review cadence, offboarding, and evidence requirements. NIST’s NIST Cybersecurity Framework 2.0 still applies, but it must be translated into machine identity operations, not reused as a human-centric checklist.
The operational risk is already clear in NHIMG research. In The 2024 Non-Human Identity Security Report, 88.5% of organisations said their non-human IAM practices lag behind or only match their human IAM maturity. That gap matters because service accounts, API keys, certificates, and agent credentials often outlive the systems they support. If they are tracked inside generic IAM reports, privilege creep, stale access, and missed revocation are easier to hide. Practitioners should treat NHIs as a separate control population with clear inventory, ownership, and lifecycle evidence. In practice, many security teams only discover hidden machine access after a breach review exposes it rather than through intentional governance.
How It Works in Practice
The practical model is to align human IAM and NHI governance at the policy level, but separate them at the control layer. Human identities can remain under workforce joiner-mover-leaver processes, while NHIs need their own inventory, approval paths, rotation standards, and reporting. That means a service account or API token should have a named technical owner, a business/system owner, a defined purpose, a time bound if possible, and a revocation trigger tied to system change or decommissioning.
For most organisations, the baseline is to move away from long-lived shared secrets and toward workload identity plus just-in-time access. Current guidance suggests using short-lived credentials, token exchange, and policy evaluation at request time rather than static role assignment alone. That is especially relevant when AI agents or automation platforms can chain actions across tools. Human-style RBAC still matters, but it should be supplemented with context-aware controls that evaluate what the workload is trying to do, where it is running, and whether the request is within its approved task scope. The Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs and Ultimate Guide to NHIs — Regulatory and Audit Perspectives both reinforce that lifecycle evidence and auditability are not optional extras.
- Maintain a separate NHI inventory with owner, purpose, system scope, and expiry data.
- Classify secrets by type, then rotate and revoke them on a machine timeline, not a human one.
- Use PAM for privileged machine access, but do not rely on PAM alone to compensate for poor identity design.
- Apply policy-as-code or equivalent enforcement so access is evaluated at runtime, not only at onboarding.
These controls tend to break down in hybrid estates where code, CI/CD, SaaS, and cloud workloads each store secrets differently because ownership and revocation become fragmented across platforms.
Common Variations and Edge Cases
Tighter NHI governance often increases operational overhead, so organisations must balance stronger control with deployment speed and service reliability. That tradeoff is most visible in environments that use shared platforms, third-party integrations, or short release cycles, where every new secret or certificate can slow delivery unless it is automated.
There is no universal standard for every edge case yet, especially for autonomous AI agents. For those systems, the main issue is not just secret sprawl but goal-driven behaviour that can expand access in ways a static human role model never anticipated. Best practice is evolving toward intent-based authorisation, ephemeral secrets, and workload identity so the agent proves what it is and what it is trying to do at the moment of access. The Top 10 NHI Issues highlights why overprivilege and weak visibility remain persistent problems, while the NIST Cybersecurity Framework 2.0 supports the broader governance discipline needed to keep human and non-human controls coherent.
Edge cases also include third-party vendors, cross-cloud workloads, and emergency break-glass accounts. Those often require exceptions, but exceptions should be time bound, logged, and reviewed like any other privileged access. Where mature automation is missing, organisations usually start with inventory and ownership first, then add rotation, then policy enforcement. A practical warning comes from NHIMG incident research such as JetBrains GitHub plugin token exposure, which shows how quickly machine credentials can escape normal governance when developer workflows are not treated as identity surfaces.
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-03 | Rotation and expiry controls are central to governing machine identities. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access management applies directly to NHI governance. |
| NIST AI RMF | AI RMF governance is relevant when autonomous agents act as NHIs. |
Inventory NHI secrets, set TTLs, and automate rotation and revocation on a machine schedule.
