How should organisations govern users, machines and agents under one access model?

Why one access model still has to treat people, machines and agents differently

The right pattern is to govern all three under a single privilege philosophy, then enforce it according to how each actor authenticates, requests access and proves ongoing authority. The model should be consistent at the policy level, but humans, NHIs and agents do not consume access in the same way, so the control points should not be identical.

That distinction matters because the wrong abstraction creates either over-restriction for people or standing privilege for non-human actors. A foundational IAM and IGA guide helps frame the shared governance layer, while the enforcement layer must reflect actor-specific risk, context and lifetime.

What the shared governance layer should standardise

The common layer should answer a few consistent questions: who owns the identity, what authority it is allowed to hold, how that authority is reviewed, and when it must be removed. That is where one access model gives you consistency across workforce accounts, service identities and agents, even if the controls underneath differ.

For humans, the model usually centres on role assignment, approval and review. For machines, the emphasis shifts toward short-lived credentials, scoped trust and lifecycle discipline. For agents, governance must also account for delegated action, because an agent can act continuously, change context quickly and trigger downstream effects faster than a person would.

This is why a human vs non-human identity comparison is useful: the governance question is shared, but the operating assumptions are not. The most mature access models treat people, machines and agents as different actor classes inside one policy system, not as one generic identity type.

How enforcement changes by actor type

Humans usually need the strongest approval and review workflow because their access is broad, episodic and easier to understand in business terms. Machines need tighter credential lifetime controls because their access is often embedded in systems, pipelines and runtime dependencies. Agents need execution-time policy because their authority can be invoked repeatedly, on different tools, with different prompts or tasks.

For agents in particular, the practical control question is not just "can this identity authenticate?" but "what can it do, at what moment, with what context, and how will we know after the fact?" That is why AI agent authorisation guidance is most valuable when access must be decided per action, not merely per account.

For non-human identities, shorter-lived secrets, tighter audience scoping and cleaner retirement become part of the access model itself. A cloud workload identity guide is relevant here because workload access is safest when authentication material is temporary, exchangeable and bound to a specific trust path rather than left to static keys.

Risk and Threat Considerations

When organisations flatten people, machines and agents into one undifferentiated access pattern, they usually create either excessive standing privilege or brittle approval bottlenecks. The security risk is not only overpermission, but also poor traceability, because the more generic the model becomes, the harder it is to tell which actor actually exercised the authority.

Failure mechanism: Persistent credentials, broad role grants or weak delegation boundaries let a machine or agent keep acting after the original need has passed, and that increases blast radius if the identity is stolen, misused or misconfigured.

Impact: The result can be unauthorised actions at machine speed, delayed detection, and access reviews that certify the policy on paper while missing the real runtime exposure.

Practitioner Guidance

What to prioritise: Define one access governance policy, then split enforcement by actor class. The policy should say how privilege is approved, reviewed and removed, while the enforcement layer should vary for human, machine and agent behaviour.

What to verify: Check whether every identity has an owner, an explicit purpose, a revocation path and a default expiry. If any actor can keep using authority without a time bound or revalidation point, the model is not really unified, it is just centrally named.

Decision rule: If the actor can initiate actions autonomously, treat execution-time controls and auditability as mandatory. If it cannot, approval and periodic recertification matter more than per-action policy.

Practitioner takeaway: The right design is one governance philosophy with three enforcement patterns, because consistency comes from shared privilege principles, not from forcing every actor to use the same control mechanics.

Related resources from NHI Mgmt Group

• How should organisations handle employees, third parties, and privileged internal users under one access model?
• How should teams govern just-in-time access across users, machines and AI agents?
• Should organisations use the same access model for humans and AI agents?
• How should organisations govern access to personal data under Quebec Law 25?