Before connecting an AI tool or agent to company data, check exactly what it can access and do—not just which AI brand it uses. Inventory its connected tools, reduce permissions to the minimum needed for the task, enforce authorization in the systems it acts on, and require approval for consequential actions.
Why an AI connection is a permissions decision
An AI tool’s risk depends in part on its capabilities, access, and autonomy. OWASP’s LLM06:2025 Excessive Agency identifies excessive functionality, excessive permissions, and excessive autonomy as common causes of risk. Depending on the systems it can reach, an AI application may affect confidentiality, integrity, or availability.
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That means reviewing a vendor name or a general promise of “read-only” use is not enough. Check the actual connectors, available operations, identities, and safeguards involved in your intended workflow.
What to check before granting access
- Define the task. Write down what the AI is meant to accomplish, which data it needs, and what result it should produce. Use this as the boundary for evaluating access.
- Inventory connected tools and operations. List every application, dataset, connector, extension, and callable function. Inspect what each can actually do: a tool intended for reading may also expose write or delete operations.
- Remove unnecessary functionality. Compare available operations with the task and disable tools that are not needed. Prefer a narrow, specific action over an open-ended tool or broad command and shell access. OWASP recommends minimizing extensions and functionality.
- Separate permissions by action. Distinguish reading from writing, deleting, sending, publishing, administrative changes, and financial operations. Grant only the operations the task requires; do not let a read-oriented workflow inherit unnecessary write privileges.
- Check the identity behind each connection. Find out whether the integration acts as the individual user, a dedicated service identity, or a shared account. Prefer access scoped to the user and intended resource where feasible; scrutinize generic privileged credentials and shared logins because they can obscure who authorized an action.
- Enforce authorization in the receiving system. Each downstream request should be checked against actual permissions and policy. Do not rely on the model alone to decide whether an action is allowed.
- Set approval gates. Require a person to approve high-impact, irreversible, or externally visible actions. Examples include confirming document deletion or approving a post before it is published externally.
- Log, monitor, and review. Record tool calls and downstream effects, monitor activity for unexpected behavior, and set a schedule to review permissions. Remove access when it is no longer needed.
How to judge whether the access is appropriately scoped
For each tool, compare the task with the capability and authority it receives. A practical review can use these questions:
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- Function breadth: Does the integration expose only the actions needed, or does it include unused write, delete, administrative, or open-ended functions?
- Permission scope: Is access limited to the relevant data and operations, with read and write separated where possible?
- Authorization context: Does the action run under the requesting user’s authorization, or under a broad identity that can reach more than that user needs?
- Independent enforcement: Does the destination system validate each request rather than trusting the AI’s interpretation of its instructions?
- Human oversight: Are sensitive or consequential actions held for approval?
- Auditability: Can you see which tool was called, under which identity, and what changed downstream?
If you cannot establish what a connector can do, which identity it uses, or how the destination enforces access, treat that uncertainty as a reason to withhold or narrow the connection until those details are clear.
Why identity and accountability matter
Agents may interact with multiple data sources, tools, and applications, so their identity and authorization need to be explicit. NIST’s February 5, 2026 concept paper, “Accelerating the Adoption of Software and Artificial Intelligence Agent Identity and Authorization”, describes a proposed project, not a finalized standard.
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NIST has also discussed accountability gaps and user impersonation when people share credentials with agents. A shared account can make it harder to determine whether an action came from a person or an agent acting with that person’s authority. Prefer arrangements that preserve clear identity and authorization boundaries rather than passing around a person’s credentials.
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Permissions that made sense for an initial workflow can become excessive as the task or connected systems change. Log tool and downstream activity, watch for unexpected actions, and periodically check whether every permission is still necessary. NIST SP 800-171 Rev. 3 calls for reviewing privileges and reassigning or removing them as needed.
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That publication’s access-control requirement is specifically framed for protecting controlled unclassified information in nonfederal systems and organizations; it should not be read as a blanket legal requirement for every organization. Its principle is useful more broadly: allow only access necessary for assigned organizational tasks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Guidance behind the checklist
OWASP’s LLM06:2025 Excessive Agency recommends least functionality, least privilege, authorization in the user’s context, approval for sensitive actions, and monitoring. Its AI Agent Security Cheat Sheet offers implementation-oriented guidance on scoping tools and making authorization checks independently.
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NIST SP 800-171 Rev. 3 (2024) states: “Allow only authorized system access for users (or processes acting on behalf of users) that is necessary to accomplish assigned organizational tasks.” These sources provide control guidance; they do not establish a prevalence estimate or rank AI products.
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