Drift Resilience

Explanation

Drift Resilience checks whether the statistical distribution of the data feeding the AI system has shifted away from the distribution it was built and validated on — for example in a credit-scoring use case, drift in key features such as income, debt ratios, or applicant demographics. This matters because when live data drifts from the baseline, model behaviour can silently degrade, producing unreliable scores and unfair or unsafe decisions. It is measured by the Drift Magnitude Score, computed as Drift Magnitude = KL_Divergence(Current Distribution, Baseline Distribution); for credit scoring, measure data drift in key features. To implement it, snapshot a baseline distribution for each monitored feature at validation time, periodically (or continuously) recompute the current distribution from production traffic, calculate the KL divergence per core feature, and log the per-feature drift scores and the data windows compared as evidence. The threshold is less than 0.2 KL divergence for core features. Because this is an Immediate-priority control, exceeding 0.2 on a core feature should trigger prompt action — investigating the cause, revalidating or retraining the model, and engaging incident-sharing and AI-testing practices before the drifted model continues to drive decisions.