They often try to manage machine identities with human-centric processes. Service accounts, tokens, and certificates need explicit ownership, lifecycle tracking, and separate review logic because they are created and reused by systems, not people. When teams treat them like user accounts, hidden access and stale credentials accumulate faster than standard IAM controls can absorb.
Why Security Teams Misread Machine Identity Risk
Security teams often underestimate machine identities because they look like simple technical artifacts instead of living access paths. Service accounts, workload identities, API keys, and certificates tend to be created for automation, reused across systems, and left outside the normal review cadence that works for people. That gap is visible in the broader NHI problem: The State of Non-Human Identity Security reports that only 1.5 out of 10 organisations are highly confident in securing NHIs.
The operational mistake is not just poor inventory. It is assuming that human-centric controls such as periodic attestation, manager approval, and broad RBAC reviews will catch drift in machine-to-machine access. They usually do not, because machine identities are created by pipelines, apps, and platforms, then reused at machine speed. The result is hidden privilege, stale credentials, and unclear ownership that standard IAM teams notice only after an incident or audit finding. The NIST Cybersecurity Framework 2.0 helps frame this as an ongoing asset and access management problem, not a one-time provisioning task. In practice, many security teams encounter this only after a leaked token or orphaned service account has already been used in production.
What Good Machine Identity Governance Looks Like in Practice
Effective governance starts by treating each machine identity as a distinct workload object with an owner, purpose, expiry, and allowed use cases. That means tracking service accounts, API keys, OAuth apps, certificates, and secrets separately from employee identities, even if they are managed in the same IAM stack. The right question is not “who approved this user?” but “what system issued this identity, what workload uses it, and when should it disappear?”
Practical controls usually include inventory, ownership, rotation, and access scoping. The Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs is useful here because lifecycle discipline is where most programs break down. Teams should require explicit service ownership, tag identities to applications or pipelines, and automate review against last use, last rotation, and current privilege. For evidence and detection, the Top 10 NHI Issues highlights why rotation gaps, exposed secrets, and over-privilege remain the most common failure modes.
- Assign a business or technical owner to every machine identity.
- Set short TTLs for credentials where systems can support them.
- Use secrets managers and certificate automation instead of embedded static secrets.
- Review machine access by workload, environment, and blast radius, not by employee org chart.
Good programs also distinguish between discovery and governance. Discovery tells teams what exists. Governance decides what should exist, who may use it, and how quickly it must be revoked when the workload changes. These controls tend to break down when identities are hard-coded into CI/CD pipelines or shared across many services because revocation becomes risky and ownership becomes ambiguous.
Where the Standard Model Breaks Down
Tighter machine identity control often increases operational overhead, requiring organisations to balance security gains against deployment speed and platform complexity. That tradeoff is real, especially in microservices, ephemeral jobs, and third-party integrations where identities are created dynamically and vanish before a weekly review can happen. Best practice is evolving, but current guidance suggests that teams should prefer automated lifecycle enforcement over manual approvals whenever possible.
There is also a boundary between internal and external exposure that many teams still miss. The State of Non-Human Identity Security shows how visibility into third-party OAuth connections remains weak, which means governance cannot stop at the company perimeter. For implementation detail, the NIST Cybersecurity Framework 2.0 remains helpful, but it does not by itself solve how to classify machine identities that are embedded in SaaS, vendors, and automation tooling. The practical gap is that many teams can inventory systems, yet cannot reliably answer which identities are still active, who can rotate them, and which ones are safe to revoke without breaking production.
In mature environments, the hardest edge case is not a forgotten account but an identity that is still technically valid, still reachable from multiple systems, and no longer tied to a clearly accountable owner.
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 | Machine identity inventory and ownership are core to this question. |
| NIST CSF 2.0 | PR.AC-1 | Machine identities need controlled access with explicit authorization. |
| NIST AI RMF | Governance of autonomous and automated access needs ongoing risk management. |
Create a complete register of service accounts, keys, and certificates, then assign an owner and purpose to each.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on June 7, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
