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No single signal is sufficient.\n\nStatistical Distribution Monitoring\nTrack statistical properties of incoming features over time using methods like the Kolmogorov-Smirnov test, Population Stability Index (PSI), or Jensen-Shannon divergence. A significant shift from training distributions is an early warning sign of data drift. This is a necessary but not sufficient condition — statistical shift in inputs doesn't always cause performance degradation, and performance can degrade without obvious input distribution changes (concept drift).\n\nPerformance Metric Tracking\nWhen ground truth labels are available (even with delay), track live model performance against a baseline: accuracy, F1, AUC-ROC, RMSE, or whatever metric governs your use case. A monotonically declining trend is the clearest signal of drift. 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Pre-deployment testing is necessary but fundamentally insufficient for detecting drift. Here's why:\n\nTests are static; the world is dynamic. A benchmark suite reflects the world at the time it was created. As the world changes, the benchmark becomes less representative.\nDrift is non-linear. Data distributions can be stable for months and then shift dramatically in days (economic shocks, viral events, regulatory changes). Quarterly retraining cycles can't respond to these dynamics.\nDrift is domain-specific. A model can degrade on a specific customer segment, product category, or geographic region while appearing healthy in aggregate metrics. Only granular, continuous monitoring reveals this.\n\nThis is precisely the thesis behind Trusys's position on AI governance: AI governance is not a one-time audit. 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