In regulated environments across the pharmaceutical, MedTech, and biotechnology sectors where AI, data integrity has traditionally evaluated through presence, completeness, and traceability of
recorded values. However, an equally consequential dimension often remains under-governed. Data gaps, whether temporal, transactional, or contextual, are frequently treated as noise, automatically interpolated, smoothed, or excluded during preprocessing. In GxP-regulated environments, such assumptions can introduce interpretive bias, obscure risk signals, and compromise decision reliability.
