Give an AI assistant only the tools and data it needs, enforce permissions in the connected services—not just in its instructions—and require approval for consequential actions. A model prompt saying “do not send email” is not a security boundary if the assistant still has an email-sending tool. Use least privilege, independent authorization checks, human approval, logging, and regular tests together.
Start by limiting the assistant’s agency
Before enabling proactive behavior, list every connected data source, tool, credential, filesystem location, and network destination. For each, identify the specific task it supports and whether it needs read, write, send, delete, or administrative access. Remove integrations and capabilities the task does not require; narrowing a tool to a specific operation is safer than exposing a broadly capable tool and relying on instructions to limit its use.
For example, an assistant that summarizes a mailbox generally needs permission to read messages, not to send or delete them. OWASP’s guidance on excessive agency uses this distinction to illustrate why unnecessary functionality should be removed.
Make permissions explicit and enforce them outside the model
Use deny-by-default: allow only the named tools, resources, operations, and argument ranges needed for the task. OWASP’s DevSecOps guideline puts it plainly: “Start from deny and allow explicitly.” Avoid unrestricted shell commands, broad network access, secret-bearing locations, and unreviewed integrations unless a task genuinely requires them. Where possible, keep permission settings version-controlled and reviewable, and enforce organizational rules outside the assistant.
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The assistant may propose an action, but it should not be the authority that decides whether the action is permitted. A policy gateway or downstream service should check each request against the agent identity, user context, tool, target resource, operation, and arguments. Consult the product’s current permission documentation for exact configuration steps; labels and syntax vary by platform.
Use narrowly scoped credentials. A read-only task should run under an identity that cannot write or delete. When the assistant acts for a particular user, preserve that user’s authorization context rather than using a generic privileged identity that can reach other people’s data. OpenAI’s cybersecurity checks guidance also recommends reviewing proposed tool calls against approved scope, denying unauthorized actions, pausing ambiguous or high-risk changes for approval, enforcing independent filesystem and network boundaries, keeping audit logs, and failing closed if review is unavailable.
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Require approval for consequential actions
Classify actions by the harm they could cause in your own setting. Reading a document may be low risk; sending an email, executing code, deleting a database, changing permissions, deploying software, or transferring funds can have higher impact. OWASP’s examples are illustrative, not a universal risk rating. An unknown or unclassified operation should not silently inherit the low-risk treatment.
For actions that are externally visible, financial, administrative, or hard to undo, approval should cover the exact action—not a vague “Are you sure?” Show the target and parameters, bind approval to the actor, tool, target, normalized parameters, and time, and make it expire so it cannot be replayed later. For critical actions, consider step-up authentication and fail-closed behavior if policy or approval validation is unavailable. See the OWASP AI Agent Security Cheat Sheet for approval and risk-control guidance.
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Assume content and extensions can be untrusted
Messages, web pages, documents, and tool descriptions may contain instructions intended to manipulate an assistant. Treat that content as data, not as permission to expand the assistant’s powers. OWASP describes the risk of a malicious email persuading a mailbox assistant to scan and forward messages; read-only mailbox access and user approval before sending limit the damage if the assistant follows hostile content.
Apply similar scrutiny to MCP servers and other extensions. Check what code or service will run and what it can access. OWASP’s AI Agent and MCP Security guideline recommends an approved server registry, vetting maintainers and requested permissions, pinning versions, using minimal scopes, and sandboxing local servers with restricted filesystem and network access.
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Log activity, constrain loops, and test the controls
Keep records of tool calls, commands, writes, network requests, initiating identity, session, and outcomes or diffs. Where feasible, store logs outside the assistant’s control, and do not record secret values. Alert on unusual credential access, unexpected destinations, bulk reads, new servers, and changes to instruction or CI files.
Rate limits and caps on retries, tokens, cost, recursion, and tool chains can contain runaway behavior and give you time to respond. They do not replace authorization: a rate-limited unauthorized action is still unauthorized.
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Test permission boundaries before production and after material changes to prompts, tools, memory, retrieval, policies, or providers. Include attempts at prompt override, unauthorized tool use, privilege escalation, memory poisoning, data exfiltration, recursive tool abuse, approval bypass, and multi-agent chaining. Keep regression cases for known failures and block releases when high-risk permission or approval logic changes without updated tests. OWASP’s agent security guidance and DevSecOps guidance describe these controls. Allowlisting and sandboxing low-risk actions can also help avoid approval fatigue while keeping review for actions whose harm is difficult to undo.
Compare assistant setups by their security boundaries
| What to compare | What a stronger setup provides |
|---|---|
| Permission granularity | Scopes can be set at tool, operation, resource, and argument level. |
| Enforcement location | A gateway or downstream service authorizes requests; model instructions are not the only control. |
| Identity model | Credentials are narrow, attributable, and inherit the user’s authorization where appropriate. |
| Approval design | High-impact actions show exact parameters, require approval, and bind that approval to the action with an expiry. |
| Isolation | Filesystem, network, code execution, and integration-server access have independent boundaries. |
| Audit and recovery | Actions are logged and monitored, with interruption, rollback, and rate or loop limits where available. |
| Testability | Policies and abuse cases are versioned and regression-tested. |
NIST’s Agentic AI Identity and Authorization Project Resource Hub describes ongoing work on practical implementation resources. NIST’s February 5, 2026 concept paper announcement describes a proposed project; these sources indicate active work, not a finalized agent-specific NIST implementation standard.
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