Start by inventorying all identity types, then enforce one governance model for provisioning, access review, logging, and revocation. Human users, service accounts, API keys, and certificates should all be tied to ownership and lifecycle controls. If a credential cannot be reviewed or removed through the same operating model, it is outside effective governance.
Why Centralized IAM Matters for Human and Machine Identities
Centralized IAM is not just an administrative preference. It is the difference between identities that can be governed and identities that accumulate silently outside policy. Human users are usually onboarded, reviewed, and deprovisioned through a known process, but machine identities often grow faster through CI/CD, cloud services, and integrations. When that happens, teams lose ownership, rotation, and revocation discipline.
NHIMG research shows how quickly this gap becomes operational: in The State of Non-Human Identity Security, only 1.5 out of 10 organisations were highly confident in securing NHIs, while 88.5% said their non-human IAM practices lag behind or only match their human IAM maturity. That gap matters because the same control failures that affect people also affect tokens, service accounts, and certificates, but at machine speed. Current guidance from the NIST Cybersecurity Framework 2.0 still applies: identify, protect, detect, respond, and recover, but the identity objects must all be inside one operating model.
In practice, many security teams discover the problem only after an old secret or orphaned service account has already been used for lateral movement or data access.
How to Operationalize One Identity Model Across People and Systems
The practical objective is not to make humans and machines identical. It is to make them equally governable. That means a single source of truth for identity inventory, ownership, access policy, logging, and revocation, even if the underlying authentication methods differ. Human identities may authenticate with MFA and SSO, while workload identities may use workload federation, short-lived tokens, or certificates. The governance layer should still answer the same questions: who owns it, what is it allowed to do, when was it last reviewed, and how is it removed?
For machine identities, the most effective pattern is to prefer short-lived credentials, workload identity federation, and just-in-time issuance over static secrets. Static API keys and long-lived certificates should be treated as exceptions, not defaults. Security teams should also connect identity records to CI/CD pipelines, cloud accounts, and SaaS integrations so orphan detection is continuous rather than periodic. The goal is a control plane that can evaluate identity lifecycle events and access requests in real time, rather than relying on separate human and machine processes.
- Inventory humans, service accounts, API keys, certificates, and OAuth apps in one register.
- Assign an accountable owner to every identity object, including non-human credentials.
- Use the same review cadence for access recertification, even if approval workflows differ.
- Log issuance, use, rotation, and revocation in one telemetry pipeline.
- Prefer ephemeral credentials and federation where systems support it.
NHIMG research highlights why this matters in the field: in The 2024 Non-Human Identity Security Report, 23.7% of organisations said they share secrets through insecure methods such as email or messaging applications. That is exactly the kind of fragmented operating model centralized IAM is meant to remove. For practical implementation patterns around exposure and privilege sprawl, see also Azure Key Vault privilege escalation exposure and JetBrains GitHub plugin token exposure.
These controls tend to break down in hybrid environments with unmanaged SaaS integrations because identities can be created outside the central platform and never re-enter it.
Where Centralization Breaks Down and What to Do About It
Tighter central IAM often increases integration overhead, requiring organisations to balance governance consistency against platform complexity. That tradeoff becomes visible in legacy systems, third-party apps, and cloud services that do not support federation, SCIM, or automated revocation. Current guidance suggests treating those gaps as exceptions with compensating controls rather than allowing them to define the operating model.
There is no universal standard for every machine-identity workflow yet. Some environments will use a PAM layer for privileged human access, while others rely on workload identity platforms or secrets managers for automation. The key is that exceptions must still be discoverable, owned, logged, and reviewable. If a certificate lives outside the inventory, or a token can be created without policy checks, centralized IAM is only partial.
Teams should also account for service identities embedded in pipelines, edge devices, and vendor-managed integrations, where deprovisioning can be delayed by dependency chains. In those cases, policy should focus on blast-radius reduction: shorter TTLs, narrower scopes, and stronger monitoring on identities that cannot yet be fully unified. The most mature programs do not force every identity into the same tool, but they do force every identity into the same governance outcome.
That distinction is what keeps central IAM from becoming a reporting exercise instead of an actual control.
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 | Inventory and ownership are core to governing non-human identities. |
| NIST CSF 2.0 | PR.AC-1 | Centralized access governance maps to managing identities and permissions consistently. |
| NIST AI RMF | GOVERN | Shared governance across humans and machines depends on accountability and lifecycle oversight. |
Define accountable ownership, review cadence, and escalation paths for all identity types under a single governance program.
