An AI agent should receive only the tools, data, and action rights needed for its assigned task. Enforce the acting user’s authorization in the systems that retrieve data or perform actions—not by asking the model to decide what is allowed—and require independent approval for high-impact operations.
What permissions should an AI agent have?
Apply least privilege to the agent as a process: restrict its access to the minimum necessary for its task. NIST’s glossary defines the principle as limiting the privileges of users or processes acting on their behalf to those needed for assigned tasks: NIST’s least-privilege definition.
That means limiting more than credentials. An agent’s available tools and functions, the records it can reach, and the actions it can take all affect risk. OWASP’s LLM06:2025 guidance treats excessive functionality, permissions, and autonomy as separate contributors to excessive agency: OWASP LLM06:2025: Excessive Agency.
Match the access to the job
- Read-only task: Grant read access to the necessary resources, not write access by default.
- Limited action task: Expose only the particular action the agent needs, rather than a broad tool with unrelated capabilities.
- Specific-user task: Restrict records to the acting user’s permitted scope instead of using an identity that can access everyone’s data.
For example, an agent that recommends products may need to read the products table, but not insert, update, or delete records. An email summarizer needs message-reading capability; it should not also receive the ability to send or delete messages unless those actions are part of its defined job and separately controlled.
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How do I limit an AI agent’s access to company data?
Start by writing down what the agent is meant to do. Turn that task into a short list of required resources and operations, then remove anything not on the list. Prefer narrowly defined functions over open-ended capabilities such as shell access or unrestricted URL fetching. Use a read-only scope when the task only requires reading.
Scope access to the relevant user, tenant, records, and sensitivity level. A connector credential that spans many users or data categories can let an agent cross boundaries even when its task is narrow. OWASP’s Cornucopia AAI6 guidance recommends minimum-access connectors, query-time authorization checks, avoiding shared accounts that enable cross-user access, and logging: OWASP Cornucopia AAI6.
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Recheck authorization for every retrieval and action
Enforce authorization when the downstream system receives each request to retrieve data or perform an action. Do not rely on a permission check made only when the agent starts, or on the model’s own assessment of whether an action is allowed. OWASP states: “Implement authorization in downstream systems rather than relying on an LLM to decide if an action is allowed or not.” Its AI Agent Security Cheat Sheet also recommends explicit authorization, per-tool scoping, review for high-risk actions, logging, and controls on tool chains.
A trusted downstream service or policy component should decide whether the specific operation is authorized for the relevant user and resource. This decision must remain effective if the model is mistaken or manipulated; the model’s interpretation of instructions is not an access-control boundary.
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Should an AI agent use my credentials?
When an agent acts for a particular user, its access should remain tied to that user’s authority and be attributable to both the agent and the delegated user where applicable. Avoid using a shared, privileged identity that obscures who authorized an operation or gives the agent access beyond the user’s rights.
Use the narrowest relevant delegated scope, and pass the identity context to the downstream authorization check. Logs should make it possible to determine which agent performed an operation, whose authority it used, what it tried to access, and what happened. The precise implementation depends on the connected system’s identity and authorization model.
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What actions should an AI agent need approval for?
Put independent approval or review in front of actions with substantial impact, external visibility, financial or administrative effect, or difficult recovery. Examples can include sending messages to other people, deleting or changing important data, and privileged administrative operations. The right boundary depends on the task, the affected resources, the action’s impact, and how easily it can be reversed.
Separate proposing an action from executing it. The agent may prepare a recommendation or request, while a trusted policy or execution component checks authorization and any required approval before the action runs. OWASP’s excessive-agency guidance recommends minimizing autonomy and requiring human approval for high-impact actions.
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How to evaluate an agent permission design
Use these questions when reviewing an agent and its connected tools. They are practical comparison criteria drawn from OWASP controls and NIST’s work on agent identity, not a single settled agent-specific access-control standard.
- Task fit: Does each tool expose only the operations the task requires?
- Data scope: Are records restricted to the correct user, tenant, and sensitivity level?
- Authorization enforcement: Is access checked for every request by a trusted downstream system or policy service?
- Identity and delegation: Can an operation be attributed to the agent and, where relevant, the human whose authority it uses?
- Impact and autonomy: Are read, reversible write, destructive, privileged, and externally visible actions treated according to their risk, with approval where warranted?
- Audit and response: Are access and authorization decisions logged, anomalies monitored, and access revocable?
Logs should support investigation without unnecessarily exposing credentials or sensitive data. Monitoring and logging are among the controls in OWASP’s AI Agent Security Cheat Sheet; NIST also identifies auditability and linking agent actions back to human authorization as design questions.
What current guidance says—and what remains open
OWASP’s LLM06:2025 and Cornucopia AAI6 offer security guidance, not a universal legal mandate. Their recommendations support task-specific tools and scopes, downstream authorization, user-context access, review for risky actions, and auditability.
On February 5, 2026, NIST’s National Cybersecurity Center of Excellence announced a concept paper on software and AI agent identity and authorization. The draft explores identification, authentication, authorization, delegation, binding human involvement to agent actions, auditing and non-repudiation, and prompt-injection mitigation. It is concept work for a potential project, not a set of final requirements. NIST describes the project as one that will produce iterative, implementation-oriented outputs: NIST announcement, NIST draft concept paper record, and NIST project resource hub.
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