Before an AI agent can use tools, set limits outside the model: grant only the permissions its task requires, check every action at execution time, require human approval for high-impact side effects, and constrain the runtime, network, and credentials. A system prompt or prompt-injection filter can help guide behavior, but neither is an authorization boundary.
1. Define what the agent is allowed to do
Write down the agent’s task and authority before connecting tools. Specify which resources it may access, which operations it may perform, what data classes are in scope, and how long the access should last. Keep the task narrow rather than giving the agent broad latitude to decide what else might be useful.
This definition becomes the basis for enforceable policy. The model’s instructions can describe the task, but permissions should be checked by the system that actually executes each tool call.
2. Minimize and scope permissions
Give the agent the smallest set of tools and permissions that can complete the defined task. Scope access to specific resources and endpoints, and separate read from write access where the tool system supports it. Avoid granting a whole tool or account broad authority when the task needs only one operation or a limited set of records. OWASP’s AI Agent Security Cheat Sheet recommends least privilege and per-tool permission scoping; OpenAI likewise advises limiting agents to data needed for the task in its prompt-injection guidance.
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3. Decide which actions need approval
Classify tool actions by their impact and reversibility. Narrow, low-impact actions may be allowed automatically under policy. Pause for explicit human review before actions that are external, financial, destructive, privacy-sensitive, difficult to reverse, or otherwise consequential. The exact boundary depends on the deployment; there is no universally applicable numeric threshold.
Show the reviewer what will happen before asking for approval: the action, target, relevant arguments, and likely consequence. OpenAI’s guardrails and human review guide describes approvals for side-effecting actions such as cancellations, edits, shell commands, and sensitive MCP actions. OWASP also recommends approval for high-impact or irreversible actions, along with action previews, audit trails, and the ability to interrupt or roll back where available. These examples are guidance, not a universal risk taxonomy.
4. Enforce policy immediately before execution
Check each proposed tool call at the boundary where it would take effect. Validate the action, arguments, target resource, identity, and permission scope against the task policy. Deny requests outside that scope, even if the model claims they are necessary.
If a policy check or required approval is unavailable, fail closed: do not execute the action. OpenAI distinguishes automatic checks from approval decisions with a concise rule: “Use guardrails for automatic checks and human review for approval decisions.” Apply that distinction in your architecture rather than relying on the agent to police itself.
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5. Isolate the runtime and protect credentials
Assume agent-generated code can access whatever files, credentials, and network connections its runtime exposes. OpenAI’s sandbox security guidance recommends isolated compute, separate workloads where data should not mix, approved outbound endpoints, and separate credential handling.
- Run agent workloads in an isolated environment rather than a broadly trusted host.
- Separate workloads when their data should not share an execution environment.
- Allow outbound connections only to destinations required for the task.
- Keep credentials separate from general agent context and limit their scope to the tools and resources that need them.
The right boundary depends on where each tool connection runs and how identities are managed in the deployment. Do not assume that isolation of one component also isolates a tool running elsewhere.
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6. Treat external content as untrusted input
Prompt injection occurs when a third party places malicious instructions in content the agent reads, such as a document or web page. That content can enter the agent’s context, but it should not be allowed to change the permissions enforced by the tool system. Narrow task instructions and restricted access reduce exposure; they do not eliminate the broader security challenge.
OpenAI’s guidance on prompt injections recommends limiting data access to what the task needs, reviewing consequential actions before confirmation, and using explicit, narrow instructions. Pair those practices with execution-time authorization and approval controls; a prompt-injection detector or carefully worded system prompt is not a substitute for either.
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7. Log decisions and review outcomes
Preserve enough information to investigate what the agent did and why. At minimum, record tool calls, policy decisions, approvals or rejections, results, and relevant network decisions. OWASP recommends audit trails, but neither its guidance nor the other cited sources establishes one logging schema or numeric risk threshold that fits every deployment.
Review these records and update controls when tools, models, tasks, or threats change. Logs are useful only if the organization can connect a proposed action with the policy decision and the resulting effect.
Quick Recap
Implementation checklist
- Define the task, allowed targets and operations, data classes, and access duration.
- Select the minimum tool set and scope permissions to specific operations, resources, and endpoints.
- Classify side effects; require preview and human approval for high-impact or irreversible actions.
- Validate every call immediately before execution, and deny it if policy or required review is unavailable.
- Isolate workloads, restrict outbound network access, and separate credentials.
- Log calls, decisions, approvals, outcomes, and relevant network activity, then review and adjust controls.
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