9 Best Practices for Securing AI Agents and LLM Applications

AI agents can do more than generate text. Depending on how they are built, they may call APIs, retrieve documents, update records, or trigger workflows. Security therefore depends on the controls around the model as much as the model itself.

Use these nine practices as a starting point for securing AI agents and LLM-powered applications.

1. Define the agent’s permitted tasks

Document what the agent is allowed to do, which data it can access, and which actions are out of scope. A clear boundary makes it easier to detect unexpected behavior.

2. Apply least-privilege access

Give the agent only the permissions needed for its assigned task. Use separate identities and narrowly scoped credentials rather than sharing a broad administrator token.

3. Validate every tool call

Do not assume a tool call is safe because an LLM generated it. Validate the tool name, arguments, resource identifiers, and requested operation against a strict schema and authorization policy.

4. Require approval for high-impact actions

Actions such as transferring funds, deleting records, changing access, or sending external communications may need explicit human approval. Use risk-based approval gates rather than relying on the model’s confidence.

5. Protect secrets and sensitive data

Avoid placing API keys, passwords, or unnecessary personal data in prompts. Store secrets in a dedicated secrets manager, restrict access, and redact sensitive values from logs and traces.

6. Enforce authorization outside the model

A model’s response is not an authorization decision. Check user and service permissions in the application and API layers every time the agent requests a protected resource or action.

7. Defend retrieval pipelines

For retrieval-augmented generation, enforce document-level access controls before content enters the model context. Track source provenance and review ingestion workflows for untrusted or altered material.

8. Monitor behavior and keep audit trails

Record security-relevant events such as tool invocation, denied actions, permission changes, and unusual request patterns. Keep logs access-controlled and avoid collecting sensitive content unnecessarily.

9. Test adversarial scenarios continuously

Test prompt injection, unauthorized data requests, malformed tool arguments, unexpected tool failures, and attempts to bypass approval steps. Repeat tests when models, prompts, tools, or permissions change.

Quick implementation checklist

• Create an explicit allowlist of tools and actions.
• Use short-lived or narrowly scoped credentials where practical.
• Validate inputs and outputs at trust boundaries.
• Enforce authorization in code, not only in prompts.
• Add approval gates to consequential actions.
• Monitor tool activity and denied requests.
• Run regression tests after each meaningful change.

Final thoughts

Secure agent design is a layered engineering problem. Combine least privilege, deterministic authorization, safe tool interfaces, monitoring, and repeatable adversarial testing. These controls reduce the chance that a model error or manipulated input becomes an application-level incident.