AI systems are entering GMP environments at pace — embedded in QMS platforms, laboratory instruments, and deviation management workflows. But the validation frameworks the industry has relied on for decades were not designed for probabilistic, opaque systems that behave differently every time they run. Traditional IQ/OQ/PQ asks: "Does the system calculate correctly every time?" For an LLM-based system, that question has no answer — the output is never exactly the same twice.

This 45-minute presentation introduces a practical, risk-based framework for qualifying AI systems in regulated pharmaceutical environments, grounded in GAMP 5, FDA Computer Software Assurance (CSA) guidance, EMA Annex 22, and the ISPE GAMP AI Good Practice Guide. The core argument is a fundamental shift in validation philosophy: stop trying to verify the algorithm, and start validating the controls around it.

The session covers: why traditional IQ/OQ/PQ is structurally incompatible with probabilistic systems; how to classify AI systems using a risk/autonomy matrix mapped to GAMP 5 Categories and Annex 22 Static/Dynamic designations; the hidden compliance risk of Shadow AI — vendor-enabled AI features running inside validated platforms without QA oversight; and the 4-Layer AI Validation Framework developed from real deployment experience: Configuration Qualification (CQ), Integration & Data Integrity Qualification (IDQ), User Interaction Qualification (UIQ), and Performance Monitoring Qualification (PMQ).

The framework is anchored by a live case study: a production AI system — a Compliance/Gap/Overlap Analyzer — that evaluates SOP sets against a regulatory RAG database. The case study covers the specific validation challenges of a two-corpus AI architecture, confidence score thresholds as acceptance criteria proxies, defect injection methodology for false negative characterization, and how continuous monitoring thresholds and revalidation triggers were established.

Attendees leave with a three-question decision framework applicable to any AI system in their environment: What is the Context of Use and what is the risk if the system is wrong? Does the AI write to a GMP record, influence a GMP decision, or neither? And what controls exist around the output that can be independently validated?