ai-safety-eval-guard

To evaluate AI safety risks like prompt injection, you define risk scenarios and conduct structured offline assessments using custom datasets. This identifies potential jailbreaking and sensitive data leakage vulnerabilities.

Defining AI risk scenarios involves identifying specific threats such as jailbreaking, prompt injection, and sensitive data leakage. These defined scenarios form the foundation for conducting structured, multi-level offline safety assessments.

Implementing guardrails to prevent harmful AI outputs involves developing and applying protective measures such as prompt constraints, tool access controls, and data sanitization rules based on prior risk evaluations.

Yes, you can run AI security evaluations using local files without external paid APIs. The evaluation process requires adherence to local file usage and explicitly avoids any external paid API dependencies.

The best way to establish a pre-launch safety gate is to systematically combine risk scenario definition, offline evaluation, and guardrail implementation to provide final safety clearance before AI deployment.

Your AI system needs protective guardrails for sensitive data to mitigate sensitive output risks and prevent data leakage. Guardrails apply data sanitization rules and prompt constraints to secure AI interactions.