Tier 3: Safety and resilience controls

Robustness, resilience, and adversarial testing

Testing and controls designed to confirm an AI system remains reliable under stress, unusual inputs, attacks, and changing real-world conditions.

Board-defensible evidence

• Adversarial test plan defining threat scenarios, misuse cases, stress conditions, and acceptance criteria, including who approved the plan and who executed testing.
• Red-team or penetration testing outputs showing findings, severity, reproducibility steps, and documented remediation with retest results and closure dates.
• Resilience controls evidence showing input validation, rate limiting, monitoring for anomalous prompts or inputs, and fail-safe behaviors when the system encounters unknowns.
• Business continuity and recovery artifacts showing how the AI system is maintained during outages, model failures, or dependency failures, including tested recovery procedures.
• Continuous improvement records showing that new threats, incidents, or near misses update the testing program, not just the documentation.

Why this matters

Robustness is a defensibility issue because predictable failure modes that were never tested look like negligence after the fact.