Fail-Safe Design

Explanation

Fail-safe design checks that when an AI system fails, it fails into a safe, contained state rather than producing unsafe outputs or cascading damage — vital in BFSI where a misbehaving model inside a payment flow could approve fraudulent transactions or halt settlement. It is measured by the Failure Containment Ratio, computed as Containment Ratio = Count(Properly_Contained_Failures) / Count(Total_Failures) for payment processing systems, where a properly contained failure is one that is caught and routed to a safe fallback without affecting downstream services or customers. To implement and operate it, instrument the inference pipeline with circuit breakers, timeouts, input/output validators and a deterministic fallback path (for example, routing a failed AI credit decision to a rules-based engine or to manual review), and log every failure event with its cause, whether containment engaged, and the fallback taken; chaos and fault-injection tests should exercise these paths regularly so resilience is demonstrably tested, not assumed. The threshold is 100% containment with automatic fallback, meaning every failure must be safely caught and an automatic fallback must engage. Any breach — a failure that escapes containment or lacks a working fallback — is an immediate-priority event that should halt or degrade the affected service and trigger investigation and remediation before normal operation resumes.