Treat human-plus-agent workflows as a single authorization problem, because the risk often sits in the combination rather than in either actor alone. A person’s clearance and an agent’s tool access can create an allowed pathway that neither should have independently. Practitioners should test the full request path, not just the separate identities.
Why Human and Agent Permissions Need One Authorization Model
Human-plus-agent workflows should be designed as one authorization surface, not as two separate approvals stitched together later. The practical issue is the combined path: a user may be allowed to request something, and an agent may be allowed to execute it, yet the joint pathway can still create more access than either actor should hold alone.
That means the key question is not “Is the human allowed?” or “Is the agent allowed?” but “Is this exact action allowed when those two permissions are combined?” In practice, that pushes teams toward intent-based policy, task-scoped delegation, and explicit limits on what the agent can do on behalf of the person.
One useful mental model is to treat the person as the originating principal and the agent as a delegated actor with narrower authority, time bounds, and action bounds. Where the workflow crosses systems, the policy should follow the request path end to end, because the risky part often appears only after the human request is translated into a machine action.
Where the Authorization Boundary Usually Breaks
The boundary breaks when teams verify identities separately but never test the combined permission path. A clean human login and a valid agent credential do not prove that the downstream action is safe, especially when the agent can chain tools, reuse context, or reach data and systems the user could not directly access.
Another common failure mode is over-broad delegation. If the agent inherits the user’s broad entitlements, or if it receives standing access “for convenience,” the workflow can quietly turn into a privilege amplifier. AI Agent Authorisation Guide is useful here because it frames task-scoped access, just-in-time approval, and per-action decisions as the baseline, not the exception.
Teams should also watch for ambiguous on-behalf-of behavior. If the workflow cannot clearly answer who requested, who approved, who executed, and which tool or resource was touched, then revocation, audit, and containment become much harder after the fact. That ambiguity is often where harmless automation becomes an accountable security incident.
How to Design, Test, and Govern the Combined Path
Good design starts by mapping the full request path: human intent, agent delegation, tool invocation, target system authorization, and the data or action outcome. If any step is “implicitly trusted,” the design is incomplete. This is especially important when an agent can act across multiple applications or can choose among tools dynamically.
Practitioners should verify three things before they trust the workflow: the agent’s own authority, the human’s allowed intent, and the exact action the pair can produce together. Zero Trust for AI Agents supports that approach by emphasizing verification of the agent, principal, and request, plus removal of standing privilege.
Governance is strongest when the agent has a separate identity posture, clear ownership, and a defined lifecycle. Agentic AI Identity Guide helps teams think about registration, delegation, and retirement as first-class controls, which matters because permissions that are easy to create but hard to retire are a recurring source of residual access.
Risk and Threat Considerations
Human-plus-agent permission combinations can create hidden privilege pathways, especially where the human’s business authority and the agent’s execution authority overlap. Attackers and careless users can exploit that overlap to trigger actions that no single identity should have been able to perform independently.
Failure mechanism: The workflow allows a legitimate person to authorize an action, then lets an agent convert that authorization into broader system access, data movement, or destructive change than the original approval intended.
Impact: This can lead to unauthorized data exposure, fraudulent transactions, lateral movement, or irreversible operational damage while still appearing to follow an approved process.
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 addresses the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Human-agent workflows can create combined privilege abuse through delegated authority. |
| ASI02 — Tool Misuse | The question concerns how agent permissions interact with tool access and execution paths. | |
| Recommendation — Enforce per-action authorization and bound delegated privilege for agentic workflows. Restrict tool scopes so agents can only invoke approved actions for the current task. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Combined human and agent permissions should be minimized to the access needed for the task. |
| IA-9 — Service Identification and Authentication | Agent execution authority depends on authenticating non-human actors and their delegated access paths. | |
| Recommendation — Limit each principal to the minimum access needed for the specific workflow step. Authenticate agent-to-system interactions before allowing delegated actions. | ||
| NIST Zero Trust (SP 800-207) | Zero Trust Architecture | The answer depends on verifying each request path instead of trusting role membership alone. |
| Recommendation — Validate each request and remove standing privilege across the human-agent path. | ||
Practitioner Guidance
What to verify: Test the combined human-plus-agent path with realistic tasks, not just the isolated roles. The control is only strong if the pair cannot reach a higher-risk action than the policy intended.
Decision rule: If the agent can touch production systems, customer data, or financial actions, require per-action authorization and a narrow delegation scope rather than standing access.
Common mistake: Teams often review the user role and the agent role separately, then assume the workflow is safe because each looks acceptable on its own. That misses the privilege created by composition.
Practitioner takeaway: The safest design is one where human intent can be delegated, but never expanded, by the agent that executes it.
Related resources from NHI Mgmt Group
- How should security teams handle AI agent visibility?
- How should security teams handle agent access that currently relies on delegated OAuth or reused human permissions?
- What is the difference between human identity governance and AI agent governance?
- What is the difference between governing human access and governing AI agent access?
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Reviewed and updated by the NHIMG editorial team on October 7, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org