Should organisations create separate policies for AI agents and human users?

Why Separate Policy Treatment Is Necessary

Human users, customer-facing bots, and internal AI agents do not fail in the same way, so a single policy model usually misclassifies risk. Humans operate with stable job functions and reviewable actions; agents operate with delegated goals, tool access, and runtime decision-making. That difference is why current guidance increasingly treats agent governance as a distinct control problem, not a minor IAM variant.

When organisations apply one policy set to both people and agents, they often either overfit to human workflows or under-specify software actor behaviour. The result is familiar: broad entitlements, weak auditability, and no clear owner when an agent crosses its intended scope. NHIMG research on the AI Agents: The New Attack Surface report shows why this matters operationally, especially when deployments expand faster than policy coverage. The same pattern appears in OWASP NHI Top 10 coverage of agentic risk.

For security teams, the practical question is not whether policies should exist, but whether they are specific enough to reflect the actor type, assurance level, and blast radius. In practice, many security teams encounter agent overreach only after the agent has already accessed data or executed actions beyond its intended scope.

How Separate Policies Work in Practice

Effective separation usually starts with identity class, not application name. A human user policy should assume interactive approval, slower change rates, and stronger identity proofing. An AI agent policy should assume delegated execution, short-lived access, and continuous evaluation of what the agent is trying to do right now. That is why frameworks such as the NIST AI Risk Management Framework and CSA MAESTRO agentic AI threat modeling framework are increasingly paired with workload identity and policy-as-code.

For agents, static RBAC is usually too blunt. Best practice is evolving toward intent-based or context-aware authorisation, where each request is evaluated at runtime against task scope, data sensitivity, and tool risk. In mature designs, the agent receives just-in-time credentials for a specific workflow, with short TTLs and automatic revocation on completion. That reduces the value of stolen secrets and limits lateral movement if the agent is compromised. The strongest implementations anchor the agent to workload identity, using cryptographic proof of what the agent is, then apply real-time policy decisions through engines such as OPA or Cedar.

• Separate approval paths for human-initiated changes and agent-initiated actions.
• Distinct policies for data access, tool use, credential issuance, and external side effects.
• Per-agent ownership, logging, and rollback procedures.
• Runtime checks for task scope, confidence, destination, and escalation conditions.

NHIMG’s CoPhish OAuth Token Theft via Copilot Studio illustrates how delegated software identities can be abused when the policy boundary is too loose. These controls tend to break down in legacy environments that cannot enforce per-request policy evaluation because the application stack only supports coarse session-based access.

Where the Boundary Gets Messy

Tighter policy separation often increases operational overhead, requiring organisations to balance governance precision against deployment speed. The hardest edge cases are shared services, hybrid workflows, and semi-autonomous assistants that sometimes behave like tools and sometimes like actors. There is no universal standard for this yet, so current guidance suggests classifying the workload by autonomy level, privilege, and external effect rather than by branding it as “AI” or “not AI.”

One common mistake is treating a customer support bot like a human service agent because both answer questions. Another is assuming internal copilots can inherit the same policy as the employee using them. In practice, the bot may chain tools, call APIs, or expose credentials in ways the human never could. That is why NHI governance pages such as the Ultimate Guide to NHIs — 2025 Outlook and Predictions remain relevant to policy design, even when the immediate question is about AI.

For high-risk environments, the rule of thumb is simple: if the actor can take actions without a human in the loop, it deserves policy treatment that assumes runtime uncertainty. If the environment cannot distinguish agent actions from human actions in audit logs or enforcement points, the separation is already too weak to provide meaningful control. This is especially true in systems where prompt injection, token theft, or tool chaining can turn a benign assistant into a privileged execution path.

Related resources from NHI Mgmt Group

• Why do AI agents create a separate data governance problem from human users?
• Why do non-human identities create compliance risk even when policies exist?
• Should organisations treat AI agents like human users in IAM?
• Why do AI agents create more cloud access risk than human users?