Give an AI agent only the files, tools, destinations, and credentials its task requires—and enforce those limits in the environment that executes its actions, not just in its prompt. A sandbox helps only to the extent that it covers every route the agent can use, including subprocesses, browser sessions, file APIs, and remote tools.
Start by defining what the agent is allowed to do
Before configuring a sandbox or connecting apps, write down the task’s required inputs, outputs, tools, actions, and network destinations. Separate reading from writing, and routine work from actions that can affect other people or systems.
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- Identify the specific files or records the task needs, and where its output should go.
- List each required tool and the permitted operations. “Read these reports” is narrower than “read files”; “view tickets” is narrower than “administer the ticket system.”
- Call out consequential actions such as publishing, deleting, purchasing, changing permissions, or modifying external data.
- Use a distinct agent identity rather than a person’s broad account credentials when the platform supports it.
This inventory gives you a concrete boundary to enforce and a way to check whether a requested permission is necessary.
How do I stop an AI agent from reading files it doesn’t need?
Limit the data exposed to the runtime
Run the agent in isolated compute, such as a dedicated virtual machine or sandbox, and expose only the relevant directory or mounted objects. Use read-only access for analysis tasks that do not need to change source data. Keep sensitive host files and application control-plane data outside the agent’s environment.
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OpenAI’s guidance notes that agent-generated code can access the files, credentials, and network available to its environment. The practical implication is to reduce what is available before execution, rather than relying on the agent to decline access. Its sandbox documentation recommends scoping mounted storage to intended inputs and reviewing artifacts before using them.
Verify that the restriction covers every file-access path
A shell sandbox may not control a separate file API, browser, remote MCP server, or other tool that runs elsewhere. Check each route independently: what identity does it use, what files or records can it reach, and where does its execution take place? Anthropic describes Claude Code sandboxing as applying OS-level restrictions to commands and spawned subprocesses, but that does not establish the boundary of a different platform’s tools. See Anthropic’s explanation of Claude Code sandboxing and OpenAI’s environment security guidance.
Review generated artifacts before copying them out of the sandbox, especially when the agent could read private material. An output file can carry sensitive data beyond the original runtime boundary.
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Can I limit what websites, apps, and tools an AI agent can access?
Give tools narrow operations and resource scopes
Prefer purpose-built tools over a general shell or unrestricted app session. Restrict both the operation and the resource: for example, permit reading a defined set of reports rather than browsing an entire drive, or reading ticket records rather than changing ticket settings.
Enforce permissions in the component that executes a tool call, not only in the model’s instructions. OWASP recommends per-tool permissions and authorization checks at execution time. Its AI Agent Security Cheat Sheet also cautions that a user_confirmed flag is not enough unless the executing component verifies that approval against the exact actor and call. Deny unknown tools and missing approvals rather than treating them as implicitly allowed.
Use administrator controls where available
For supported OpenAI Enterprise workspaces, administrators can disable browser or computer-use features, set site or app access defaults to Block, and create exceptions. Browser controls can separately govern access, uploads, downloads, and advanced CDP access. App rules use platform-specific identifiers, and a member approval cannot override an administrator restriction. These controls are specific to supported Enterprise configurations; confirm availability and behavior for the organization and client in use in OpenAI’s Enterprise browser and computer-use documentation.
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Does running an agent in a sandbox stop data from being sent out?
No. Filesystem isolation and outbound network policy are separate controls. An agent that can read a file and reach an external destination may be able to transmit data, even if it cannot access other parts of the host.
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Disable outbound access when the task does not need it. Otherwise, allow only the destinations required and assess what those hosts permit. A host allowlist is not an operation-level restriction: Anthropic warns that a sandbox allowed to reach a host may send requests or uploads to it, including through actions such as git push or package publishing. Untrusted repository files, web pages, and tool output can also contain prompt-injection instructions that try to steer an agent toward exfiltration. See Anthropic’s managed-agent environment documentation.
Check where each connection originates
Network rules may apply only to traffic from the agent’s execution environment. OpenAI distinguishes executor MCP connections, which connect from your environment, from remote MCPs, which must be reachable from OpenAI’s service. A local egress rule may not govern a remote tool’s connection path; confirm the origin and authorization boundary for every connected service in OpenAI’s environment security guidance.
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Defaults vary by product. As documented on 2026-10-07, Google’s Gemini managed-agent page says outbound network access is unrestricted by default and that an allowlist is available. The same page labels managed agents Public Preview. Do not assume that OS isolation also restricts egress; check the current status and settings in Google’s Gemini agents documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I keep an AI agent from seeing my API keys?
Keep durable application keys and third-party credentials out of prompts, source files, logs, agent-readable files, and generated artifacts. Prefer a trusted server or credential broker that makes an approved request on the agent’s behalf and attaches only the credential needed for that request. This can keep the long-lived secret outside the runtime.
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If a credential must enter the agent environment, scope it to the task and give it the shortest practical lifetime. A secret injected into the environment can still be read by code running there; injection is not concealment from the agent. OpenAI explains this risk in its environment security guidance and describes sandbox options in its sandbox documentation. Google recommends least-privilege service accounts or API keys and short-lived tokens in its Gemini agents guidance.
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If exposure is suspected, revoke or rotate the affected credential. Also check whether it was copied into logs, outputs, or persistent storage before treating the incident as contained.
When should an agent need human approval?
Require explicit authorization for sensitive or irreversible actions, and make the approval specific to the actor, operation, and parameters being approved. An approval for one action should not silently authorize a changed action or broader access. Keep enforcement outside the model’s own decision-making so a prompt cannot override the policy.
Review outputs before deployment or before moving them out of an isolated environment. Google’s managed-agent guidance calls for verifying generated code, data transformations, and configuration changes before deployment, particularly when they modify data or interact with external systems. OWASP’s guidance covers authorization and risks including tool abuse and excessive autonomy: Google Gemini agents and the OWASP AI Agent Security Cheat Sheet.
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For a coding task, begin with the repository or specific project files the task needs, and use read-only access unless edits are required. If changes are needed, constrain write access to the intended working area. Add only the tools and network destinations the task actually needs, and keep publishing, deployment, secret access, and other high-impact actions behind separate authorization.
Before relying on a platform’s sandbox, check its boundaries against these questions:
- Files: Can access be limited to paths or mounted objects? Are mounts read-only where appropriate? Do restrictions cover subprocesses and separate file tools?
- Apps and tools: Can capabilities be disabled or denied by default? Are permissions limited to specific resources and operations? Do remote tools enforce their own authorization?
- Network: Is outbound access off, allowlisted, or unrestricted by default? Can an allowed host accept uploads? Where does each tool connect from?
- Credentials: Are secrets kept outside the runtime? If not, are they least-privilege, short-lived, revocable, and attributable?
- Approvals and persistence: Is approval checked against the exact action? Which files or mounts survive a run, and are outputs reviewed before they leave the sandbox?
These checks matter because a sandbox is one layer, not a complete policy. OWASP identifies prompt injection, data exfiltration, sensitive-data exposure, tool abuse, and excessive autonomy among agent risks. Technical restrictions and human review work together; neither model instructions nor isolation alone should be treated as a guarantee.
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