Distribution shift is the silent regulator in production AI

A model validated on historical data degrades when production inputs drift. In pharma, this is not just a performance issue; it is a regulatory one. Ongoing monitoring is a GxP expectation, not an optimization. The model that passed qualification last year may produce subtly wrong outputs this quarter because the input distribution shifted.

The defense combines tracing (what the model actually did in production) with periodic re-evaluation (does it still pass the qualification eval suite?). Drift detection catches the shift before it causes harm. This extends the tracing and evaluation serve different jobs principle: tracing is the ongoing performance verification; evaluation is the periodic re-qualification. Both are required; neither alone is sufficient.