AI red‑teaming exercises probe systems for failure modes and inform mitigations. The work emphasizes responsible testing: scoped scenarios, reproducible prompts, and careful handling of outputs. Findings are documented with recommended fixes and follow‑up evaluations to verify improvements. Over time, the practice builds confidence in real‑world robustness without overclaiming results.