Use separate entitlement models for humans and agents, even when they operate in the same tool. Human access, technical team access, and agent permissions should be reviewed differently because they create different risk profiles, different approval needs, and different revocation triggers.
Why Shared Platforms Need Separate Access Models for Humans and Agents
When a platform serves both people and AI agents, the access model has to reflect two different actors, not one blended user class. Humans usually sign in for judgment, review, and exceptions. Agents operate under delegated authority, execute faster, and can trigger many more actions per hour, so the same permission model creates avoidable blast radius.
The practical distinction is not cosmetic. A human account may justify broad interactive access with strong approval and session controls, while an agent often needs narrowly scoped, task-based access with tighter expiry, stronger policy checks, and clearer ownership. Treating both as “users” hides the real risk boundary and makes reviews, revocation, and incident response harder.
That is why teams should separate entitlement design even when the same tool is shared. If an AI agent can act in production, its permissions should be explicit, short-lived where possible, and bound to the action it is allowed to perform. Human access and agent access may live in the same platform, but they should not share the same assumption set.
How to Review Human, Team, and Agent Access Differently
The review process should also be separated by actor type. Human access is usually assessed through role, function, and business need. Technical team access often reflects operational duty and break-glass conditions. Agent access should be checked against purpose, delegation, and the specific action surface it can reach, because an agent can misuse a permission at machine speed or under a bad prompt, bad tool call, or bad workflow state.
In practice, that means the question is not just “does this account need access?” but “what kind of actor is this, who owns it, and what decision governs its continued access?” For agents, ownership and revocation need a tighter feedback loop than for people. The control objective is to prevent standing privilege from becoming hidden automation privilege.
Where the platform exposes shared capabilities, separate review queues or approval paths are often cleaner than a single entitlement list. That helps reviewers spot when a human role, a support role, and an agent role all look similar on paper but carry different operational consequences. A uniform recertification process often misses the one permission that is harmless for a person and dangerous for an agent.
Designing Revocation and Escalation Around Different Risk Profiles
Revocation is where mixed human-agent platforms often fail. Human access is usually removed when employment, role, or need changes. Agent access should also be removed when the task ends, the integration changes, the model behavior drifts, or the upstream approval is withdrawn. If the revocation trigger is the same for both, the agent is likely to keep privileges longer than intended.
Escalation should follow the same logic. A human exception may require manager or system owner approval, while an agent exception may require product, security, and service ownership because the blast radius is broader and harder to explain after the fact. Shared platforms need this distinction so that temporary access does not quietly become persistent delegated authority.
Good design also means the platform can tell which actions were performed by a person and which were performed by an agent on behalf of a person or team. Without that separation, audit evidence, incident reconstruction, and access review all become weaker than the actual risk demands.
Risk and Threat Considerations
Shared human-agent platforms create a mixed-trust environment where a permission that is acceptable for an interactive user can become dangerous when exercised by automation. The main risks are privilege creep, weak revocation, and over-broad delegation, especially when agent actions are fast, repetitive, or triggered by untrusted input.
Failure mechanism: Teams collapse human and agent entitlements into one model, then apply the same approval and review logic to both. That hides the different abuse paths, makes standing privilege harder to see, and increases the chance that an agent keeps access after the original business need has ended.
Impact: An agent can execute larger volumes of activity than a human, so a single excess permission can produce faster data exposure, unauthorized changes, or wider operational damage before detection and revocation catch up.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address 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 |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Shared human-agent entitlements can mask privilege abuse and delegated authority risk. |
| Recommendation — Separate agent entitlements from human roles and enforce per-action authorization. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | AI agents sharing platforms with humans can accumulate excessive permissions and standing access. |
| Recommendation — Scope agent access narrowly and remove any privilege not required for the task. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Service Identification and Authentication | Agent access to shared platforms relies on authenticating non-human actors distinctly from people. |
| AC-6 — Least Privilege | Separate entitlement models are a least-privilege requirement when agents and humans coexist. | |
| IA-5 — Authenticator Management | Revocation and review differences depend on managing agent credentials separately from human ones. | |
| Recommendation — Use distinct service authentication and lifecycle controls for agent access. Limit each actor to the minimum access needed for its role and task. Track, rotate, and revoke agent authenticators independently from human credentials. | ||
Practitioner Guidance
What to prioritise: Start by splitting inventory into human, technical team, and agent-owned entitlements, then identify which permissions are truly shared and which only appear shared because they sit in the same platform. The critical test is whether the actor can act autonomously or only through interactive judgment.
Decision rule: If the entitlement can change production state, access sensitive data, or trigger downstream automation, treat agent access as a separate approval and revocation class even when the underlying tool is the same. If you cannot explain why the permission is safe for autonomous use, it is not ready to be shared.
What good looks like: Each agent has a named owner, a scoped purpose, a review cadence matched to its task duration, and a clear kill path. Human reviews should not be used to “cover” agent permissions, and agent approvals should not be buried inside standard user provisioning.
Practitioner takeaway: The safest shared platform is not the one with one universal entitlement model, it is the one that makes human judgment, technical administration, and machine delegation visibly different.
Related resources from NHI Mgmt Group
- How should teams govern external authentication when users and AI agents share the same platform?
- Why do AI agents make non-human identity governance harder?
- Why do AI agents create new risk in non-human identity management?
- Why do AI agents increase non-human identity risk in existing IAM programmes?