They should compare it with both, because AI platforms combine human user access, workload-style credentials and delegated tool use. The right question is not which model wins, but whether the governance model covers who can access the platform, what it can reach, and how quickly access is removed when circumstances change.
Human IAM and workload identity are both relevant, but for different reasons
AI access governance is not a clean fit for only one identity model. Human IAM matters because people approve, operate, and sometimes interact directly with AI platforms. Workload identity matters because the platform itself, its services, and its integrations often authenticate like software systems, with tokens, keys, or federated credentials.
The comparison should therefore start from the access pattern, not the label. If the control question is who signs in, who approves, and who can change policy, human IAM is central. If the control question is how services, agents, or automation reach tools and data, workload identity is the better analogue.
That is why access governance for AI should usually be read through both lenses at once. A platform can be well governed for users yet still be weak on service-to-service trust, or strong on machine credentials yet poor at user review, approval, and recertification.
What changes when AI platforms behave like both people and systems
AI platforms blend interactive access and delegated execution. A person may open the door, but the system may then call APIs, retrieve data, invoke tools, or act across multiple back-end services. That combination means the effective access model often spans identity and access management basics, agent identity and delegation, and traditional workload authentication patterns.
For practitioners, the useful comparison is not “human IAM or workload identity”, but “which part of the AI stack is being governed at this moment?” Human IAM is strongest for account lifecycle, approval chains, and user accountability. Workload identity is strongest for machine authentication, token use, and service-to-service trust boundaries.
This is also where access scope becomes the decisive control. If the AI platform can reach production data, internal APIs, or administrative tools, then the governance model must track not only the signed-in user but also the runtime permissions attached to the platform and its connected services.
Where comparison breaks down, and how to think about lifecycle
AI governance breaks down when organisations treat a single login as if it explains the whole trust model. In practice, access may outlive the user session through cached credentials, delegated tokens, connector grants, or a service account carrying broader reach than the human who initiated the action.
That is why lifecycle control matters as much as authentication. A useful comparison is whether access can be discovered, reviewed, rotated, and removed at the same speed as the underlying business context changes. NHIMG’s NHI lifecycle management guide is a good reference point for the operational side of that problem, especially where machine-style credentials and delegated access are involved.
In a mature model, user access reviews and machine credential governance should converge around the same outcome: no standing access that cannot be explained, no delegated privilege that cannot be traced, and no token or key that outlives its need.
Risk and Threat Considerations
AI platforms create compound access risk when human approvals and machine permissions are not governed together. The failure mode is usually not a single bad password, but a chain: a user gets access, the platform inherits broad tool permissions, and those permissions continue after the user no longer needs them.
Failure mechanism: Overly broad delegation, long-lived credentials, or weak offboarding can let AI systems keep reaching data and tools after the original business justification has ended.
Impact: The result can be data exposure, unintended action execution, privilege escalation, or lateral movement across connected services, especially where the AI platform can call administrative or production APIs.
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 addresses the attack and risk surface, while NIST SP 800-53 Rev 5 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST SP 800-53 Rev 5 | IA-2 — Identification and Authentication (Organizational Users) | AI platform users and approvers need strong human authentication. |
| IA-9 — Identification and Authentication (Non-Organizational Users) | Covers external users and service-style authentication patterns around AI access. | |
| IA-5 — Authenticator Management | AI access often depends on token, key, and credential lifecycle control. | |
| Recommendation — Enforce strong user authentication for AI platform access and administration. Apply appropriate authentication controls for non-organizational access paths. Manage AI credentials, tokens, and keys with rotation and revocation. | ||
| OWASP Non-Human Identity Top 10 | NHI-01 — Improper Offboarding | Delegated AI and machine access must be removed when the need ends. |
| Recommendation — Revoke AI-related credentials and grants promptly during offboarding. | ||
Practitioner Guidance
What to prioritise: Define access governance by function, not by identity type. Map human approval, runtime delegation, and machine authentication as separate control layers, then make sure each layer has an owner and a review cadence.
What to verify: Check whether the AI platform’s effective permissions exceed the signed-in user’s intent. If a user can trigger actions that persist through tokens, connectors, or service credentials, treat that as a workload-governance problem as well as a human access problem.
Decision rule: If the question is about who may use the AI interface, start with human IAM. If it is about what the platform may do after login, start with workload identity. In most real deployments, you need both views before the access model is trustworthy.
Practitioner takeaway: The right governance model for AI is layered, because user access explains permission to start the action, while workload identity explains how far the action can reach once it begins.
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 do organisations compare agent identity platforms with access governance needs?
- How should organisations govern AI agents alongside human identity and device access?