Manual review breaks when the number of service accounts, APIs, and AI-driven identities grows faster than the team can verify ownership, purpose, and necessity. At that point, access recertification becomes stale before it is completed, and risky entitlements remain active because reviewers lack enough context to make confident decisions.
Why This Matters for Security Teams
Manual identity review is not just slow. It becomes structurally unreliable once machine identities outnumber the people assigned to approve them. Service accounts, API keys, workload tokens, and agent identities often lack a clear business owner or current inventory record, so reviewers are forced to make judgment calls with partial evidence. That creates stale approvals, missed revocations, and a false sense of control.
NHI Management Group’s Ultimate Guide to NHIs notes that only 5.7% of organisations have full visibility into their service accounts, which explains why manual review degrades so quickly in large environments. This is also where broader control frameworks such as NIST Cybersecurity Framework 2.0 matter: they assume identity governance is continuous, not a periodic paperwork exercise.
The practical risk is that unused or over-privileged machine identities remain active long after their purpose has changed, especially in CI/CD, cloud, and AI-driven systems. In practice, many security teams encounter the blast radius only after a credential leak, incident review, or failed audit reveals that the recertification queue was already obsolete.
How It Works in Practice
Effective review for machine identities starts with replacing manual sampling with inventory-backed, event-driven governance. The review process needs to know what the identity is, what workload it serves, who owns it, what it can reach, and whether its privileges still match the task. Without that context, recertification turns into guesswork.
Current guidance suggests using a combination of centralized discovery, workload classification, and automated evidence collection. For NHI programs, that means pulling data from cloud IAM, secrets managers, CI/CD systems, and observability tooling into a single review workflow. The Top 10 NHI Issues research highlights how visibility and lifecycle gaps commonly drive review failure, while 52 NHI Breaches Analysis shows that identity compromise frequently compounds when access is left in place after the original use case is gone.
- Use ownership metadata so every machine identity maps to a team, service, or workload.
- Automate recertification with policy rules for age, privilege level, last use, and environment.
- Prefer ephemeral credentials and short TTLs where the workload allows it.
- Flag identities with no observed activity for review or revocation.
- Treat AI agents and other autonomous systems as dynamic workloads, not static users.
This is where manual review breaks down in highly dynamic environments because access patterns change faster than reviewers can validate them, especially when identities are created and destroyed by pipelines, orchestration layers, or agentic workflows.
Common Variations and Edge Cases
Tighter review controls often increase operational overhead, so organisations have to balance assurance against reviewer fatigue and system churn. That tradeoff becomes sharper when machine identities are short-lived, highly distributed, or created by code rather than by humans.
One common edge case is delegated ownership. A service account may be technically owned by one team but functionally controlled by another through a deployment pipeline. Another is dormant but necessary access, where a workload runs only monthly or under failover conditions. In those cases, current guidance suggests using exception handling with expiry dates rather than leaving access permanently approved.
For autonomous AI agents, the problem is even harder because the identity review question is no longer only “who owns this?” but also “what can this agent decide to do at runtime?” That is why Ultimate Guide to NHIs — What are Non-Human Identities is best read alongside the emerging control logic in frameworks such as NIST CSF. There is no universal standard for fully manual recertification of machine identities at scale, and organisations that rely on it usually discover the gap after a credential has already been overexposed or reused across systems.
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 | Manual review fails without identity inventory and ownership clarity. |
| NIST CSF 2.0 | PR.AC-1 | Access governance depends on verified identity and entitlement context. |
| NIST AI RMF | Autonomous systems need ongoing governance when behavior changes at runtime. |
Build an authoritative NHI inventory with owner, purpose, and lifecycle data before any recertification cycle.
Related resources from NHI Mgmt Group
- What breaks when machine identity lifecycle management is still partly manual?
- What breaks when machine identity changes do not propagate across systems?
- How should security teams govern non-human identities in cloud environments?
- Why do non-human identities create audit risk in modern environments?
