The GxP–AI Governance Gap: Why Validated Data Infrastructure Does Not Produce Trustworthy AI Decisions in Regulated Life Sciences

Regulated life sciences organizations have spent more than a decade learning to validate their data infrastructure. Governed data lakehouses, medallion lineage, immutable audit trails, and risk-based Computer Software Assurance now give auditors a defensible, traceable path from a regulatory submission back to the raw source record. That is a genuine achievement. It is also widely mistaken for something it is not: assurance that the artificial-intelligence systems now making and informing decisions on top of that data are themselves trustworthy. This paper argues that validated data infrastructure and trustworthy AI decisions are different problems, separated by what the author terms the GxP-AI governance gap: the distance between a platform whose data lineage is validated and a decision whose model behavior is not. Drawing on the clinical-validation literature, which has long distinguished algorithm testing from clinical validation, the paper shows why the governing GxP principle, that the record must reflect reality, breaks when a model sits between the data and the decision, and why the major AI-specific regulatory instruments already presuppose a correspondence that a validated platform alone does not establish. It then specifies a deployment-time assurance layer, reframing data lineage as decision lineage, that closes the gap.