What are the security considerations for deploying AI agents?
Input and Output Security
AI agents are vulnerable to prompt injection, where malicious input tricks the agent into performing unintended actions. Sanitize and validate all inputs, and use techniques like input filtering and output encoding. Never trust the model's output blindly; validate it before acting on it.
If your agent can execute code or call external tools, sandbox those operations. Limit the agent's permissions to only what it needs. For example, if the agent only needs to read a database, don't give it write access.
- Use allowlists for tools and actions the agent can perform.
- Implement rate limiting to prevent abuse.
- Sanitize user inputs to remove malicious content.
- Validate and sanitize model outputs before using them.
- Log all actions for auditing.
Data and Access Security
Protect sensitive data by encrypting it in transit and at rest. Use secure storage for API keys and secrets, such as a secrets manager. Rotate keys regularly. Implement authentication and authorization for any API endpoints the agent exposes.
Be cautious about what data you send to external model providers. If privacy is critical, consider local models or providers with strong data protection agreements. Also, ensure compliance with regulations like GDPR or HIPAA if applicable.
Common mistakes
- Assuming the model will always behave as intended and not implementing guardrails.
- Hardcoding API keys in code or configuration files.
- Giving the agent broad permissions to internal systems without restrictions.
