agent-safety-guard

To prevent prompt injection in AI agents, implement defense-in-depth architectures across input, system, tool, and output layers. This approach combines input validation, system prompt isolation, tool-safety controls, and output monitoring to resist untrusted adversarial inputs.

Defense-in-depth for AI agent security is a multi-layered architecture pattern that applies safety guardrails across input, system, tool, and output layers. It protects production agents against jailbreaks, data leakage, and uncontrolled tool use by layering multiple independent controls.

Run red-team testing on an AI agent by applying attack simulation checklists against your prototype to validate defenses. This process executes prompt injection and jailbreak scenarios to identify vulnerabilities in your agent's safety guardrails before deployment.

Yes, you can use these safety guardrails for incident response planning by defining monitoring, alerting, and governance strategies for production agents. The architecture helps detect, contain, and mitigate uncontrolled tool use and data leakage during active incidents.

The best way to monitor AI agents for data leakage is to implement governance strategies with dedicated alerting and monitoring controls at the output layer. This detects unauthorized data exfiltration attempts during tool use and system execution in real-time.