Organisations should separate them whenever service accounts, API keys, tokens, or certificates are part of the access estate. Human identity controls are built around user behaviour and interactive authentication, while NHI governance must address secrets, standing privileges, rotation, and offboarding. Treating them as the same model leaves machine access under-governed.
Why This Matters for Security Teams
The separation point is not philosophical, it is operational. Human IAM assumes interactive users, predictable login flows, and approval paths that can be reviewed by managers or access committees. Non-human identities behave differently: they authenticate through secrets, certificates, OAuth grants, and workload tokens, often at machine speed and across services. That makes human-centric processes a poor fit for rotation, revocation, and offboarding of machine access.
Current guidance from the NIST Cybersecurity Framework 2.0 reinforces that access governance should match the asset and risk profile being controlled, not just the account owner. NHIMG research shows why this matters in practice: in the Ultimate Guide to NHIs, the lifecycle of machine identities is treated as a distinct governance problem, not a user-identity variant. In practice, many security teams encounter excessive standing access only after a token, API key, or certificate has already been reused outside its intended scope.
How It Works in Practice
Organisations separate the models by assigning different control owners, policies, and tooling to human identities versus machine identities. Human IAM remains focused on authentication strength, user provisioning, joiner-mover-leaver workflows, and role assignment. nhi governance takes ownership of the full machine identity lifecycle: discovery, classification, issuance, rotation, revocation, and continuous monitoring.
In practice, that means the security team should treat service accounts, API keys, OAuth grants, certificates, and workload tokens as governed credentials rather than user accounts. A practical control model usually includes:
- Inventorying NHIs separately from employees and contractors.
- Applying shorter TTLs and automated rotation to secrets and tokens.
- Using workload identity where possible so systems prove what they are, not who created them.
- Routing approvals through application or platform owners instead of HR-driven workflows.
- Reviewing standing privilege and dormant credentials on a machine-identity schedule, not a user-access schedule.
This is also where policy architecture matters. Human IAM often relies on static role assignment, but machine access is better governed with context-aware controls evaluated at request time, especially when workloads are ephemeral or distributed. The 2024 ESG Report: Managing Non-Human Identities notes that 72% of organisations have experienced or suspect an NHI breach, which underlines the need for dedicated visibility and lifecycle control. Best practice is evolving, but current guidance suggests NHI governance should sit alongside IAM and feed into it, not disappear inside it. These controls tend to break down when legacy applications hard-code long-lived secrets because revocation becomes dependent on application change windows.
Common Variations and Edge Cases
Tighter separation often increases operational overhead, requiring organisations to balance stronger control against platform complexity and developer friction. That tradeoff is real, especially where teams are migrating from ad hoc scripts and shared service accounts into formal identity governance.
There is no universal standard for exactly where the boundary should sit, but current practice generally separates by authentication method and risk. For example, a human who can approve deployments still belongs in human IAM, while the deployment pipeline, container runtime, or API integration belongs in NHI governance. Shared tooling can exist, but the policy model should not be shared by default.
Edge cases appear in low-code tools, robotic process automation, and admin automation where a person initiates an action that is executed by a machine identity. In those cases, the initiating human and the executing NHI both need governance, because the human decision and the machine privilege are not the same control object. The Top 10 NHI Issues highlights that over-privilege and poor rotation remain recurring failures, while 52 NHI Breaches Analysis shows how quickly that gap becomes an incident pattern. Organisations that are still using the same approval and review cadence for both identity types usually find the separation problem only after machine access has already outgrown human oversight.
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 and CSA MAESTRO address the attack and risk surface, while 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 | Separating machine and human IAM starts with distinct NHI inventory and governance. |
| CSA MAESTRO | M1 | MAESTRO addresses identity, access, and governance for autonomous and machine workloads. |
| NIST AI RMF | AI RMF governance applies where automated systems and agents consume NHIs. |
Inventory NHIs separately and assign lifecycle controls to machine identities, not user IAM.
Related resources from NHI Mgmt Group
- What is the difference between human IAM controls and NHI governance?
- What does the 144:1 NHI-to-human ratio mean for IAM governance programmes?
- How should organisations delegate user and group management without weakening IAM governance?
- What should organisations measure to know if IAM governance is actually working?
