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Give an AI agent only the files, tools, destinations, and credentials its task requires—and enforce those limits in the environment that actually executes its actions. Instructions alone are not a security boundary: code an agent runs can access resources exposed to that environment. Isolate execution, scope file and tool permissions, control network egress, keep durable secrets outside the runtime, and require authorization for consequential actions.
Start by defining what the agent needs to do
Before configuring permissions, describe the task in terms of resources and actions. A request such as “review these reports and draft a summary” may need read access to a particular folder and permission to create one output file; it does not inherently need access to the rest of the computer, an administrator session, or the public internet.
Record the task’s inputs, output locations, tools, permitted operations, and necessary network destinations. Distinguish reading from writing, and ordinary work from actions that publish, delete, purchase, change permissions, or modify an external system. Where supported, use a separate agent identity rather than exposing a person’s broad account credentials. OWASP’s AI Agent Security Cheat Sheet recommends enforcing authorization in the component that executes a tool, not relying on the model’s own decision-making.
Restrict files and the execution environment
Expose only the required files
Run the agent in an isolated environment, such as a dedicated VM or sandbox, and provide only the relevant directory or mounted objects. If the task only calls for analysis, make those inputs read-only. Keep sensitive host files and application control-plane data outside the environment. When the agent creates artifacts, inspect them before copying them to a more trusted location. OpenAI’s sandbox guidance recommends scoping mounted storage to intended inputs and reviewing artifacts before use.
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This boundary matters because, as OpenAI’s security documentation puts it, “Agent-generated code can access the files, credentials, and network available to its environment.” Treat every file or mount made available to the runtime as potentially accessible to agent-generated code.
Check which file-access paths the sandbox covers
A sandbox may restrict shell commands without restricting every other way an agent can reach data. Verify separately whether its controls apply to subprocesses, platform file APIs, browser or computer-use features, remote tools, and delegated agents. Anthropic describes Claude Code’s sandbox as using OS-level restrictions for commands and spawned subprocesses; that does not establish what a different platform’s tools or remote services enforce. Check the documentation for each execution path you enable.
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Limit tools and app permissions
Prefer purpose-built tools over broad access
Give the agent narrow operations against named resources—for example, “read these reports” instead of “read any file,” or “read tickets” instead of “administer the ticket system.” For every tool, define the allowed operations and resource scope. Deny unknown tools and missing approvals by default, and enforce the policy inside the tool server or other component that performs the action.
An approval signal is not enough on its own. OWASP warns that a user_confirmed flag is meaningful only if the executing component verifies the approval against the exact actor and tool call. Require fresh approval if a consequential action or its parameters change. See the OWASP guidance on agent authorization.
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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 rules can separately address access, uploads, downloads, and advanced CDP access. App rules use platform-specific identifiers, and a member approval cannot override an administrator restriction. These controls depend on the organization and client configuration in use; consult OpenAI’s Enterprise browser and computer-use controls to confirm availability and current settings.
Control where the agent can send data
Disable outbound network access when the task does not need it. Otherwise, allow only the destinations required, and assess what the agent can do at each one. A host allowlist limits destinations, not necessarily operations: a permitted host might accept uploads, and an agent could use an allowed route to send data out. Anthropic specifically notes that allowed hosts may receive uploads, including through actions such as git push or package publishing; untrusted repository files, web pages, or tool output may also contain prompt injection that steers an agent toward exfiltration. See Anthropic’s managed-agent environment documentation.
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Check the network path for each tool, not just the sandbox’s local egress rules. OpenAI distinguishes executor MCP connections, which originate from your environment, from remote MCPs, which must be reachable from OpenAI’s service. A local firewall rule may not govern a remote tool’s connection. OpenAI’s sandbox security documentation describes this distinction.
Check product-specific network behavior
| Platform documentation | What it establishes | Practical implication |
|---|---|---|
| Google Gemini managed agents | Outbound network access is unrestricted by default; a network allowlist is available. The documentation labels managed agents Public Preview. | Do not infer restricted egress from sandbox isolation. Set and verify the allowlist, and recheck behavior and preview status before deployment. |
| Anthropic managed-agent environments | Network access is controlled by host, and an allowed host may accept requests including uploads. | Review each host’s purpose and the operations it permits; a host allowlist is not an upload-prevention policy. |
| OpenAI agent environments | Executor MCP and remote MCP connections use different network origins. | Apply controls to the actual connection origin; local egress policy may not cover a remote MCP. |
These are documented product behaviors, not universal defaults for all versions, plans, or configurations. Confirm the settings for the specific service and deployment you use.
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Keep durable credentials out of the agent runtime
Do not put long-lived API keys or third-party credentials in prompts, source code, agent-readable files, logs, or generated artifacts. Prefer a trusted server or proxy that brokers requests and attaches only the credential and permissions needed for an approved destination. This keeps the durable secret outside the agent’s environment.
If a credential must enter the runtime, use the narrowest scope and shortest lifetime the task supports. Assume code running there can read an injected secret; OpenAI explicitly warns that storing a secret and injecting it into the environment does not hide it from agent-generated code. Google recommends least-privilege service accounts or API keys and short-lived tokens. See OpenAI’s security guidance and Google’s Gemini agents documentation. If exposure is suspected, revoke or rotate the credential.
Require review for consequential actions
Put authorization outside the model’s own decision process. For sensitive or irreversible operations, require approval that identifies the current actor, exact operation, and parameters. Do not treat a general “approved” state as permission for a different action. Review generated code, data transformations, configuration changes, and other artifacts before deployment or before moving them out of the sandbox—especially if they can modify data or interact with external systems. Google’s managed-agent guidance also recommends verifying these outputs before deployment.
Evaluate the complete permission boundary
Before adopting an agent platform or changing its configuration, check how it handles each of these areas. Ask for concrete settings and enforcement behavior, not only a general claim that the agent is sandboxed.
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- Filesystem: Can access be scoped to specific paths and operations? Are mounts read-only where appropriate? Do restrictions cover subprocesses and every file-access tool?
- Apps and tools: Can administrators deny capabilities by default and allow specific apps or resource-level operations? Do remote tools use separate authorization?
- Network: Is egress disabled, limited to named hosts, or unrestricted by default? Can allowed hosts receive uploads, and where do tool connections originate?
- Credentials: Can secrets remain outside the runtime? If not, are they narrowly scoped, short-lived, revocable, and attributable to a distinct identity?
- Approvals: Are approvals enforced by the executing component and bound to a specific actor and operation? Can an alternate tool or saved approval bypass the intended restriction?
- Persistence and review: Which files or mounts survive a run? Are artifacts inspected before leaving the environment, and can you audit policy decisions and tool actions?
These controls reduce exposure, but they do not make the model’s behavior itself a security boundary. OWASP identifies prompt injection, tool abuse, data exfiltration, excessive autonomy, and sensitive-data exposure among agent risks; technical restrictions and human review address different parts of that risk.
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