Artificial intelligence is moving from experimental pilots to embedded infrastructure across regulated domains such as audit, finance, and professional services. As these systems begin to make or influence decisions that carry strategic, financial, and reputational risk, their reliability can no longer be assured by static validation alone. This white paper presents a framework for Reliability & Repair: a structured, repeatable process for detecting, triaging, simulating, repairing, and verifying failures in complex AI systems. By combining established reliability-engineering practices with modern AI monitoring techniques, it demonstrates how organizations can measure reliability growth, align risk with severity, and transition from passive oversight to continuous improvement.
