Should organisations use one model for human and AI identity governance?

Why a Single Governance Model Breaks Down

Human identity governance and AI identity governance solve related but different problems. People move through employment, contractor, and role changes, so controls center on joiner-mover-leaver events, access review, and separation of duties. AI identities can be agents, service accounts, or workload identities that act at runtime, so governance must also account for secrets, delegated permissions, tool access, and machine-to-machine trust.

The practical mistake is to force the same control language onto both populations. A human access review is usually about whether a person still needs access. An AI identity review has to ask whether the identity is still needed, what it can call, which secrets it can use, and whether its runtime permissions match the current use case.

That is why a single operating model only works at the policy level. The policy can set shared principles such as least privilege, ownership, and review cadence, but the enforcement logic has to differ for humans and AI identities if the organisation wants controls that actually reflect how access is granted and used.

What Should Stay Shared, and What Must Stay Separate?

The strongest pattern is one governance framework with separate control sets. Shared policy gives the organisation one language for ownership, approval thresholds, evidence retention, and escalation. Separate control sets then translate that policy into the right lifecycle steps, approval paths, and monitoring rules for each identity type.

That split also helps with inventory and accountability. A unified inventory lets security, IAM, and application teams see both people and AI identities in one place, but the record structure should distinguish a person, a service account, a bot, or an agent. Without that distinction, review workflows become noisy, exceptions get hidden, and owners lose sight of which identities can act autonomously.

The same separation applies to trust boundaries. Human identities typically rely on user authentication and role assignment, while AI identities often depend on credentials, token handling, API access, and machine-to-machine trust. A governance model that treats those paths as equivalent usually underestimates how fast AI permissions can spread across systems.

How to Design Governance So It Scales for Both Populations

Use a common governance layer to define ownership, risk thresholds, and reporting, then implement population-specific controls underneath it. For humans, that usually means employment-linked provisioning, periodic recertification, and prompt removal at departure or role change. For AI, it means tighter lifecycle control over creation, rotation, offboarding, secret handling, and runtime authorization.

A useful design principle is to govern the capability, not just the account. If an AI identity can invoke tools, reach production data, or trigger downstream workflows, those powers need explicit approval and monitoring even when the underlying account looks like ordinary machine access. IAM and IGA Basics is a useful reference point for aligning access governance with separate lifecycle and entitlement logic.

For AI-heavy environments, the governance model should also support the way agents are actually built and operated. Inventory, ownership, review, and decommissioning need to be visible at the system level, not only at the user level. That is where AI governance and identity governance meet, and why the controls have to be coordinated rather than merged.

Risk and Threat Considerations

Using one unchanged model creates a control mismatch. Human-focused workflows tend to miss long-lived secrets, overprivileged service accounts, and autonomous agent permissions, while AI-focused workflows can over-automate decisions that should still require human approval. The result is either blind spots or friction, and both increase exposure.

Failure mechanism: When the same review and lifecycle process is applied to both populations, organisations often inherit stale entitlements, hidden machine credentials, and weak ownership over AI actions. That creates a path for privilege creep, credential misuse, and unreviewed machine-to-machine access.

Impact: Compromised or overbroad AI identities can reach production systems, move laterally through trusted integrations, and make changes at machine speed before human reviewers notice. The risk is not just unauthorised access, but loss of control over how far that access can spread.

Practitioner Guidance

What to prioritise: Start by separating governance policy from control execution. Keep one policy framework, but define different lifecycle, approval, and monitoring rules for humans and AI identities so the operating model stays coherent without becoming generic.

What to verify: Confirm that your inventory can distinguish human users from AI identities, service accounts, and agent credentials. If your system cannot show ownership, runtime permissions, and secret dependency separately, your governance process will not scale cleanly.

Decision rule: If an identity can act without a person present, treat it as an AI or non-human governance case and require controls for secrets, runtime scope, and machine trust. If the identity exists only to represent a person, keep it in the human access review path.

Practitioner takeaway: The right model is not one process for everyone, it is one governance standard with different enforcement mechanics for different kinds of identity.

Related resources from NHI Mgmt Group

• Should organisations use a dedicated AI agent identity model or extend current NHI controls?
• Should organisations use one governance workflow for humans, NHIs, and AI agents?
• What breaks when organisations use one IAM model for humans and non-human identities?
• When should organisations use AI-driven decision support in identity governance?