No. They are linked, but they are not governed the same way. People authenticate, devices establish trust, SaaS applications need discovery and certification, and AI agents require explicit ownership and lifecycle controls. A single process will miss one of those dimensions.
People, devices, SaaS apps, and AI agents solve different access problems
IAM teams should not collapse these four actor types into one control model. They all request access, but they do so with different proof, governance, and failure modes. People usually need interactive authentication and session controls, devices establish trust through posture or device identity, SaaS apps depend on inventory and certification, and AI agents need explicit ownership, scoped authority, and lifecycle management.
The practical test is whether the access decision depends on who or what is acting, how trust is established, and how quickly that trust can be withdrawn. If those answers differ, the control model must differ too.
For AI agents, the distinction is especially important because a delegated actor can hold authority without behaving like a human user. NHIMG’s AI Agent Authorisation Guide treats task-scoped access, per-action policy, and human approval as separate design choices, not optional refinements. That same separation is why Agentic AI Identity Guide focuses on registration, delegation, ownership, and retirement rather than only login mechanics.
Where the access model diverges by actor type
People are usually governed by authentication strength, MFA, session duration, and entitlement review. Devices are different because the control question is whether the endpoint, host, or managed device can be trusted enough to participate in access at all. That means posture, attestation, certificate trust, and revocation matter more than interactive login prompts.
SaaS applications are another category entirely. They are often discovered through SSO, OAuth, or admin visibility, then certified based on business need, data exposure, and operational ownership. A discovered app can be legitimate yet still be an unmanaged access path if no one owns the grant, the tenant, or the connected data flow.
AI agents add a fourth pattern: an actor that may act on behalf of a person, but not under the same assumptions as the person. An agent can be authenticated, delegated, over-scoped, or retired independently of the human who launched it. The control failure is usually not “can it sign in?” but “can it act only within its intended scope, and can we prove who owns it?”
This is why a single joiner-mover-leaver workflow or a single access request form will usually under-control at least one of the four groups. NHIMG’s Top 10 NHI Issues is useful here because it frames discovery, ownership, rotation, and offboarding as separate lifecycle problems rather than one generic access issue.
What teams should standardise, and what they should not
Standardise the governance layer, not the actor assumptions. A common inventory, common approval record, and common logging model are useful. But the approval logic, revocation trigger, and evidence required to trust the actor should vary by population.
For example, people need identity proofing and authentication assurance. Devices need managed trust and device-level revocation. SaaS apps need discovery, certification, and review of connected permissions. AI agents need explicit ownership, per-action authorisation, and retirement controls so that delegated authority does not become standing authority.
The strongest operating model is to map each access path to the right control family, then make exceptions visible. NHIMG’s AI Agent Observability, Audit and Incident Response Guide is a good example of why this matters: once an agent exists, logging and attribution become part of access governance, not just incident response.
External guidance points in the same direction. The OWASP Agentic AI Top 10 treats identity and privilege abuse as a distinct class of agentic risk, while the OAuth 2.0 Authorization Framework remains the baseline reference for delegated machine access patterns that are not equivalent to human login.
Risk and Threat Considerations
When IAM teams force all four populations into one process, the usual failure is over-trusting one actor type while under-governing another. That creates blind spots such as stale SaaS grants, unmanaged device trust, excessive agent permissions, or human credentials being reused by automation.
Failure mechanism: A generic access workflow can miss the actor-specific control that actually constrains abuse, so standing access, weak ownership, or poor offboarding persists even when the organisation believes it has a single source of truth.
Impact: The result is larger blast radius, slower revocation, weaker attribution, and a higher chance that a compromised user, device, SaaS app, or agent can move farther than intended.
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, OWASP Agentic AI Top 10 and OWASP API Security Top 10 address the attack surface, NIST SP 800-53 Rev 5 sets the technical controls, and ISO/IEC 27001:2022 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 — Improper Offboarding | AI agents and SaaS apps need ownership and retirement controls. |
| NHI-05 — Overprivileged NHI | Delegated actors can accumulate standing privilege beyond their task scope. | |
| NHI-10 — Human Use of NHI | The question centers on not treating human and non-human access the same. | |
| Recommendation — Define offboarding triggers and revoke access immediately when the actor is retired. Limit non-human access to task-scoped permissions and remove unnecessary standing rights. Separate human workflows from non-human workflows and prohibit credential reuse across them. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | AI agents require explicit ownership, scoped authority, and delegation controls. |
| Recommendation — Enforce per-action authorisation and restrict agent privileges to the minimum required. | ||
| OWASP API Security Top 10 | API2 — Broken Authentication | SaaS apps and delegated access paths often depend on correct token and login handling. |
| Recommendation — Validate authentication flows and token handling for every connected application path. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Service Identification and Authentication | Devices, SaaS apps, and AI agents authenticate as non-human actors. |
| AC-2 — Account Management | Different actor types need different provisioning, review, and removal processes. | |
| AC-6 — Least Privilege | The answer hinges on scoped access differing across users, devices, apps, and agents. | |
| Recommendation — Authenticate non-human actors with distinct credentials and rotate them on a defined schedule. Manage each account population with actor-specific provisioning, review, and disablement rules. Assign only the permissions each actor type needs to complete its approved function. | ||
| ISO/IEC 27001:2022 | A.5.15 — Access control | This is an access-governance question requiring differentiated control of multiple actor types. |
| A.8.5 — Secure authentication | People and non-human actors rely on different authentication and trust mechanisms. | |
| Recommendation — Define access rules that distinguish people, devices, applications, and agents. Use authentication methods matched to the actor type and trust model. | ||
Practitioner Guidance
What to prioritise: Separate your inventory and review logic by actor type before you try to unify dashboards. If the review question is “who owns this?” for one class and “can this device still be trusted?” for another, the same workflow is already too coarse.
What to verify: Every AI agent and SaaS app should have a named business owner, a technical owner, and a revocation path. If you cannot identify all three, treat the access path as incomplete even if SSO is working.
Common mistake: Teams often equate “non-human” with “service account” and stop there. That misses the fact that devices, SaaS apps, and AI agents each change the control problem in different ways, so one policy template will not fit all.
Practitioner takeaway: Treat IAM as a shared governance plane with separate actor-specific controls, not as one universal access pattern, because the failure mode is usually control mismatch, not missing authentication.
Related resources from NHI Mgmt Group
- How should security teams govern access when AI agents and humans share the same apps?
- How should security teams govern access when users, devices, SaaS apps, and AI tools all create entry points?
- How should teams govern external IAM when APIs and AI agents share the same access boundary?
- Should teams treat agentic AI access as a PAM or IAM design problem?
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Reviewed and updated by the NHIMG editorial team on October 10, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org