Tier 3: Safety and resilience controls

AI in safety-critical systems

AI used in environments where failures can cause injury, loss of life, major infrastructure disruption, regulatory shutdown, or contract termination.

Board-defensible evidence

• Safety case or hazard analysis documenting plausible failure modes, severity ratings, and mitigations, including who approved the safety assumptions and when they were last reviewed.
• Operational constraints documentation defining where AI is allowed to act autonomously, where it must defer to humans, and what conditions force a safe state.
• Verification and validation evidence showing testing under representative operating conditions, edge-case testing, and documented results reviewed by safety, engineering, and risk leadership.
• Fail-safe and rollback procedures showing how the system is halted or reverted, who can trigger it, how quickly it must happen, and how it is tested periodically.
• Incident reporting and post-event review artifacts showing how near misses and failures are logged, investigated, escalated, and used to update controls and operating limits.

Why this matters

In safety-critical contexts, governance must prove that controls were designed for harm prevention, not just performance optimization.