They should govern them under one architecture but not treat them as identical identities. Human access, service accounts, and delegated AI access create different timing, review, and containment problems. A workable programme keeps shared policy intent while distinguishing how access is issued, monitored, and revoked for each actor type.
Why one IAM programme should cover both humans and machines
What changes is not the governance model itself, but the operating assumptions behind it. Human users, service accounts, workloads, and delegated AI access all consume entitlements, yet they differ in proofing, review cadence, approval authority, and revocation triggers. Treating them under one programme gives you a single policy and control plane, while still allowing different lifecycle rules by actor type.
A useful mental model is “one architecture, multiple identity patterns.” That lets teams standardise policy intent, logging, and ownership while avoiding the common failure of forcing human-style joiner-mover-leaver workflows onto machine access that is created and retired by code, pipelines, or orchestration. It also helps prevent the opposite mistake, which is allowing machine access to escape the discipline applied to workforce access.
The practical distinction is in how access is issued and contained. Human access is often mediated by interactive authentication, approvals, and periodic recertification. Machine access is usually short-lived, non-interactive, and tied to workload, environment, or tool execution. Delegated AI access adds another layer, because an agent may act with borrowed authority, so the programme must track who granted the authority, what scope was granted, and when it should expire.
For teams building this model, the strongest pattern is to separate policy from implementation. The policy can say that all access must have an owner, a purpose, a review path, and a revocation path. The implementation can then differ: workforce accounts may use strong interactive controls, while service accounts and delegated access may rely on workload identity, tightly scoped tokens, or just-in-time access. The architecture stays unified even when the mechanics do not.
Where the unified model pays off
The main benefit is consistency. When humans and machines are managed in different silos, organisations usually end up with different inventories, different approval chains, and different definitions of “current access.” That creates blind spots, especially where a machine identity is created by one team, used by another, and forgotten by both. A shared programme reduces that drift by making ownership and control expectations explicit across all actor types. See the Identity Security Programme Guide for how to structure that operating model.
It also improves lifecycle hygiene. Machine access often fails not because it was never approved, but because it was never revisited after deployment changes, environment changes, or pipeline changes. Human access tends to be reviewed on a calendar; machine access tends to fail on process gaps. A combined programme helps teams apply the same questions, such as who owns it, what does it reach, how is it rotated, and what event should cause removal. That lifecycle lens is central to the NHI Lifecycle Management Guide.
Good programmes also recognise that the access class matters. A user account, a workload identity, and a delegated agent token may all appear as “an identity,” but they create different containment problems. Human access failures are often about excessive entitlement or weak authentication. Machine access failures are often about long-lived credentials, reuse, and broad trust boundaries. The best practice is to normalise the controls, not the actors. Human vs Non-Human Identity is a helpful reference point for that distinction.
How to keep shared policy from becoming shared confusion
The hardest part is not defining the programme, it is avoiding false equivalence. If teams use one policy document but still review every access grant the same way, they will either over-control machine access or under-control human access. The right approach is to align on common governance fields, owner, purpose, scope, expiry, monitoring, and revocation, then set different rules for evidence and cadence by actor type. That is where a broader identity programme can help, especially when it explicitly spans workforce, machine, and AI-agent access.
For cloud and platform teams, the cleanest implementation is usually to anchor machine access in workload identity and ephemeral credentials rather than static secrets. That reduces standing exposure and makes revocation more reliable, because the access path can die with the workload or expire with the token. Human access, by contrast, should remain strongly attributable to a person and should preserve approver evidence and recertification records. The architectural difference is well illustrated in the Cloud Workload Identity Guide.
Another useful control point is privilege. Shared policy intent does not mean shared privilege levels. If machine access is granted the same standing rights as a human admin account, the blast radius usually becomes larger than the business intended. A practical programme therefore uses the same entitlement governance process, but different right-sizing methods, especially where service accounts, API access, or delegated agent access can reach production systems.
Risk and Threat Considerations
When human and machine access are managed as if they were the same thing, the usual failure mode is overexposure: standing secrets, broad entitlements, and weak ownership clarity. Attackers and internal misuse both benefit from that confusion, because a forgotten service account or a delegated token often has fewer behavioural checks than a human user and may evade normal review cycles.
Failure mechanism: Organisations apply workforce-style review to machine access, or machine-style automation to human access. That breaks containment when non-interactive access is long-lived, when delegated authority is not time-bound, or when revocation depends on manual cleanup after deployment changes.
Impact: Excessive access persists longer than intended, which increases the chance of privilege abuse, lateral movement, and hard-to-detect misuse across production systems, pipelines, and tools.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST SP 800-53 Rev 5 and CSA Cloud Controls Matrix set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Machine and human access both depend on secure credential lifecycle and rotation. |
| IA-9 — Service Identification and Authentication | Machine, service, and delegated access are central to the human-versus-machine distinction. | |
| AC-6 — Least Privilege | The question is about keeping one programme while right-sizing different access patterns. | |
| Recommendation — Apply IA-5 to manage issuance, rotation, storage, and revocation of all authenticators. Apply IA-9 to authenticate services, workloads, and other non-human actors separately from users. Apply AC-6 to right-size access by actor type and prevent standing overprivilege. | ||
| CSA Cloud Controls Matrix | IAM — Identity & Access Management | The topic is fundamentally about IAM operating model and control separation by identity class. |
| Recommendation — Use IAM to govern workforce and non-workforce access under one control framework. | ||
Practitioner Guidance
What to prioritise: Build one inventory and one ownership model first, then split the control treatment by actor type. If you cannot answer who owns an account, what it reaches, and how it is revoked, the programme is not ready for finer-grained policy.
What to verify: Check whether your current recertification process can distinguish interactive human access from non-interactive machine access. If it cannot, you are probably overtrusting calendar reviews and underchecking workload and delegated access paths.
Common mistake: Teams often unify reporting before they unify lifecycle controls. That creates the appearance of governance without fixing the actual revocation and containment problem.
Practitioner takeaway: One programme should govern all access, but the controls must fit the actor, because the security problem is not “identity” in general, it is how quickly each kind of identity can be proved, limited, monitored, and removed.
Related resources from NHI Mgmt Group
- What should IAM teams measure when human and machine access share the same platform?
- How should identity teams govern human and machine access in the same programme?
- How should teams decide whether CIAM belongs in the same IAM programme as workforce access?
- How should security teams govern human and non-human access in the same programme?
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Reviewed and updated by the NHIMG editorial team on October 11, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org