Validating Artificial Intelligence Frameworks in GxP Environments: A Practitioner's Handbook

Validating AI in a GxP environment isn't like validating ordinary software, and that's exactly where most teams get stuck.You know IQ, OQ and PQ. You know how to prove fixed software does what it should. But an AI system learns, drifts, and makes decisions that touch product and patient, and the usual playbook doesn't tell you how to validate it or keep it compliant. The regulations are still catching up, and the first AI-related GMP findings have already landed.This handbook removes the confusion. It gives you one clear, practical method, built on the CSV and CSA foundation you already use.It answers what you're actually stuck on:How much do I need to validate a given AI system, and how do I defend that call?How do IQ, OQ and PQ work for a model that keeps learning?How do I handle data drift, retraining and decision integrity?What evidence will an inspector ask for, and how do I build it?One real example runs the whole way through, from intended use to an inspection-ready evidence pack. You also get 28 working templates, a 120-question inspection bank, and a draft Annex 22 crosswalk.Written for QA and quality leaders, IT compliance practitioners, regulatory peers, and the executives who sponsor the work. Grounded in ICH Q9, EU Annex 11, 21 CFR Part 11, FDA Computer Software Assurance, the EU AI Act, and the draft Annex 22.Sachin Bhandari has more than 25 years in pharmaceutical Quality IT and validation, and has taken AI-supported quality systems through health-authority inspection with no compliance findings.If AI has reached your GxP floor and you're not sure how to validate it, start here. Read more