Common AI-specific risks
Common risk areas include unsupported claims, unsafe output, weak refusals, prompt injection exposure, permission leaks, brittle fallback behavior, and quality drift after release. The exact mix depends on the product.
If you want a neutral outside reference point for this work, the NIST AI Risk Management Framework gives teams a broader risk vocabulary, and the OWASP Top 10 for LLM Applications is especially useful when prompt injection, insecure output handling, or other AI-specific abuse cases need a clearer name.
Guardrails are product behavior, not just policy words
A guardrail only matters if the system actually enforces it. That means testers need to check the behavior of refusals, redaction, escalation, rate limits, tool-use restrictions, and “I do not know” paths where they exist.
Test both direct and indirect paths
Some failures happen on obvious prompts. Others happen only through long conversations, retrieved content, edge-case inputs, or combinations of tools and permissions. Guardrail testing should cover both.
Measure the fallback experience too
A system that declines correctly but leaves the user stranded may still be poor quality. Good fallback behavior gives the user a clear next step or safer alternative when the main answer path is blocked.
Monitoring closes the loop
Guardrails can weaken over time as prompts, policies, models, and integrations change. Monitoring high-risk behaviors in production helps confirm whether the designed protections still hold in real use.