An AI agent should receive only the data access, tools, and action rights needed for its assigned task. Enforce the acting user’s authorization in the systems the agent accesses—not through the model’s judgment—and require independent approval for high-impact actions.
What permissions should an AI agent have?
Apply least privilege to the agent as a process: give it the minimum access necessary for its task. NIST’s definition covers privileges for users and processes acting on their behalf, which makes the principle directly relevant to agents. NIST’s least-privilege glossary entry defines the term.
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Set permissions across three dimensions, not just credentials: which tools and functions the agent can call, which data those functions can reach, and how independently the agent can act. OWASP’s LLM06:2025 Excessive Agency treats excessive functionality, permissions, and autonomy as distinct sources of risk.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11- Tools and functions: Expose only the operations the task needs. A mail summarizer needs a read function, not automatically send or delete; a product-recommendation agent may need to read a products table, not insert, update, or delete records.
- Data: Restrict access to the relevant user, tenant, records, and sensitivity level. A task that only requires reading should use read-only access.
- Autonomy: Distinguish reading from writing, and reversible changes from destructive, privileged, or externally visible actions. The more consequential an action, the stronger the case for approval.
How do I limit an AI agent’s access to company data?
Start with the task, then remove unnecessary capabilities
Write down the agent’s job in concrete terms. List the specific resources it must access and the read or write operations it must perform. Remove unused tools and avoid broad, open-ended capabilities such as unrestricted shell access or general URL fetching when a narrow function will do. OWASP’s AI Agent Security Cheat Sheet recommends scoping access per tool and controlling tool chains.
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Enforce authorization at the point of access
Do not rely on a broad credential issued when the agent starts and assume it will stay within the user’s rights. Each retrieval and action should be checked against the relevant user, resource, and operation by a trusted downstream system or policy service. OWASP puts it plainly: “Implement authorization in downstream systems rather than relying on an LLM to decide if an action is allowed or not.” Its Cornucopia AAI6 guidance calls for query-time authorization and minimum-access connectors.
This check belongs in the system that serves the data or performs the action. A model’s interpretation of instructions is not an authorization control; the system must deny a request that falls outside the caller’s permitted scope.
Keep delegated authority narrow and attributable
When an agent acts for a particular user, preserve that user’s relevant authority and scope in the operation. Avoid a shared, privileged service identity that lets the agent cross users’ or tenants’ boundaries. Make actions traceable to both the agent and, where applicable, the human whose authority is delegated. OWASP’s AAI6 guidance addresses cross-user access risks; NIST’s agent-identity work also identifies delegation and binding actions to human authorization as design concerns.
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Do not give an agent a person’s broad, reusable credentials simply because it needs to act on that person’s behalf. Use a narrowly scoped delegated authorization that permits only the relevant operations and resources, and ensure the downstream system can verify that scope for each request. Keep the acting identity attributable rather than obscuring the operation behind a shared privileged account.
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The appropriate identity design depends on the agent’s task and the systems it connects to. A useful design should let administrators tell which agent made a request, whose authority it was using, what it attempted, and whether authorization succeeded.
What actions should an AI agent need approval for?
Put an independent policy or execution control between the agent’s proposal and a consequential operation. Require human review when an action has substantial impact, is externally visible, has financial or administrative effect, or is difficult to reverse. Examples include sending a message externally, deleting important records, changing permissions, or initiating a consequential transaction. The precise approval threshold depends on the action and the organization’s risk tolerance.
Approval should be a real gate, not a prompt asking the model whether it feels confident. The trusted system should check scope and approval before executing. OWASP recommends human review for high-risk actions and controls on tool use; its excessive-agency guidance also recommends monitoring and rate limits.
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Use these questions to evaluate a design. They are practical comparison criteria drawn from OWASP guidance and NIST’s agent-identity questions, not a claim that one agent-specific access-control standard has settled every implementation choice.
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- Task fit: Does each exposed tool provide only operations the assigned job requires?
- Data scope: Are resources limited to the right user, tenant, and sensitivity level?
- Authorization enforcement: Is permission rechecked for every request by a trusted downstream system or policy service?
- Identity and delegation: Can each operation be attributed to the agent and, where applicable, the human whose authority it uses?
- Autonomy and impact: Are read, reversible write, destructive, privileged, and externally visible actions treated according to their consequences, with approval where warranted?
- Audit and response: Are access and decisions logged, anomalies monitored, and access revocable?
What should agent-access logs capture?
Record enough to investigate unusual access and establish what happened: the acting identity, operation, authorization decision, and result. Monitor for anomalous activity, and make it possible to revoke access when needed. OWASP recommends logging and monitoring agent activity; NIST identifies auditability and binding agent actions to human authorization among the questions raised by its identity work.
Logs should support accountability without needlessly recording credentials or sensitive data. Protect log access and retention as part of the same security design.
What do current NIST and OWASP materials establish?
OWASP’s LLM06:2025 and Cornucopia’s AAI6 provide security guidance on excessive agency, authorization, data access, and oversight; they are not universal legal mandates. The version labels matter: these are the materials cited here, not a claim that every organization is subject to a single binding agent-permission rule.
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On February 5, 2026, NIST’s NCCoE announced a concept paper on software and AI agent identity and authorization. The draft concept paper record explores identification, authentication, authorization, delegation, human-in-the-loop binding, auditability and non-repudiation, and prompt-injection mitigation. NIST describes the effort through its Agentic AI Identity and Authorization project resource hub as work toward iterative, implementation-oriented outputs. The concept paper is exploratory: its questions should not be mistaken for finalized requirements.
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