Validating AI in GxP

Program Overview

Introduction

Artificial Intelligence and Machine Learning are rapidly transforming pharmaceutical manufacturing, quality control, and decision-making processes. However, the unique characteristics of AI systems present fundamental challenges to traditional validation approaches. This comprehensive training program provides a deep and practical understanding of how to validate, govern, and maintain AI systems in GxP-regulated environments.

The program integrates the latest regulatory frameworks, including EU GMP Annex 11 (Computerised Systems), the new Draft Annex 22 (Artificial Intelligence), FDA’s Computer Software Assurance Guidance, 21 CFR Part 11 (Electronic Records; Electronic Signatures), and the ISPE GAMP Guide: Artificial Intelligence. It addresses the critical question: “How do you validate something that can learn?”

The 2025 revision of Annex 11 expands from 5 pages to 19, with 17 sections, introducing much more specific, practical, and up-to-date requirements. It is supplemented by the new Annex 22, which for the first time provides a dedicated regulatory framework specifically for AI models in the GMP environment. The ISPE GAMP Guide: Artificial Intelligence (published July 2025) bridges established GAMP concepts with the unique characteristics of AI.

Participants will learn why traditional Computer System Validation breaks when applied to AI and ML, how to classify and tier AI systems based on risk, how to build evidence that withstands regulatory scrutiny, and how to keep AI models valid long after deployment through ongoing monitoring, drift detection, and retraining governance.

The program uses a rich blend of explanatory text, key bullet points, tables, case studies, and practical examples to solidify understanding. Each module concludes with a knowledge check, and a comprehensive final test ensures mastery of the entire curriculum.

Target Audience

This program is designed for professionals across the life sciences and related industries who have responsibility for AI systems in GxP-regulated environments.

• Validation Engineers and CSV Specialists
  Professionals responsible for validating computerized systems will learn how to adapt traditional validation approaches to the unique challenges of AI and ML systems, including data governance, model acceptance criteria, and ongoing performance monitoring. The program provides practical guidance on redefining Installation Qualification, Operational Qualification, and Performance Qualification for AI and ML models.
• Quality Assurance Professionals
  QA managers, directors, and specialists will gain a comprehensive understanding of regulatory expectations for AI in GxP environments. This knowledge enables them to review and approve AI validation packages with confidence, establish effective AI governance frameworks, and prepare for regulatory inspections of AI systems.
• Data Scientists and AI and ML Engineers
  Technical professionals developing AI models for GxP use will understand the regulatory requirements that must be met. The program covers data governance, test data independence, explainability, confidence scoring, and change control requirements that apply to AI systems in regulated environments.
• Regulatory Affairs Professionals
  Those who prepare submissions and interact with regulatory agencies regarding AI-enabled systems will understand the evidence expectations and documentation requirements for AI validation. This includes understanding how to present AI validation evidence in regulatory submissions.
• IT and Cybersecurity Professionals
  Personnel responsible for the infrastructure supporting AI systems will understand the security, access control, and data integrity requirements applicable to GxP AI systems. This includes understanding network segmentation, firewall requirements, and cybersecurity considerations for AI systems.
• Senior Management and AI Program Leaders
  Leaders responsible for AI strategy and governance will gain the knowledge needed to establish effective AI governance programs, allocate resources appropriately, and manage regulatory risk. The program provides guidance on building an AI program from the ground up.
• Auditors and Inspectors
  Professionals responsible for auditing AI systems will understand the key areas to focus on, including data governance, test data independence, explainability, drift monitoring, and change control. The program includes inspection question banks and common findings.

Key Learning Objectives

Upon completion of this program, participants will be able to do the following:

• Articulate the fundamental differences between traditional software validation and AI validation, and explain why AI breaks the traditional CSV model.
• Interpret and apply the key regulatory frameworks governing AI in GxP, including EU GMP Annex 11, Draft Annex 22, FDA CSA Guidance, 21 CFR Part 11, and the ISPE GAMP Guide: Artificial Intelligence.
• Classify and tier AI systems based on risk to patient safety, product quality, and data integrity, and apply proportionate validation efforts.
• Establish AI governance frameworks, including operating models, validation master plans, and cross-functional oversight structures.
• Build comprehensive evidence for AI validation, including data governance, performance acceptance criteria, test data management, and the three independences.
• Redefine IQ, OQ, and PQ for AI and ML systems, adapting traditional qualification phases to the unique characteristics of machine learning models.
• Implement ongoing monitoring programs for AI systems, including drift detection, performance regression monitoring, and bias assessment.
• Manage explainability and confidence requirements, including feature attribution, confidence scoring, and threshold setting.
• Control changes to AI models through formal change control, configuration management, and retraining governance, including Predetermined Change Control Plans.
• Prepare for regulatory inspections of AI systems, including common findings, inspection question banks, and evidence readiness.
• Govern the use of Generative AI and Large Language Models in non-critical GxP applications with appropriate human-in-the-loop oversight.
• Build an AI program from the ground up, including first 100 days planning, governance design, and QMS integration.

Program Certification

Upon successful completion of this program, including the final test, participants will be awarded a Certificate of Completion. This certification demonstrates a robust understanding of AI validation in GxP environments, enhancing professional credentials and career opportunities. The certification is valid for three years, after which participants are encouraged to update their knowledge through continuing education activities.

Program Structure

The program is divided into six comprehensive parts, each containing multiple modules, building on the knowledge gained in previous sections.

Part 1: Getting Your Bearings

• Module 1: AI in GxP — The Concepts That Matter for Validation
• Module 2: The Regulatory Spine — Annex 11, Draft Annex 22, Part 11, and CSA
• Module 3: Why AI Validation Breaks Traditional CSV

Part 2: Classify and Govern

• Module 4: Classification and Risk Tiering
• Module 5: The AI Governance Operating Model
• Module 6: The AI Validation Master Plan

Part 3: Build the Evidence

• Module 7: Data Governance for AI
• Module 8: Performance Acceptance Criteria
• Module 9: Test Data and the Three Independences
• Module 10: IQ, OQ, and PQ Redefined for AI and ML

Part 4: Keep It Valid Over Time

• Module 11: Ongoing Monitoring and Drift Response
• Module 12: Explainability and Confidence
• Module 13: Change Control and Retraining
• Module 14: AI Risk Management Beyond the Tier Model

Part 5: Prove It and Scale It

• Module 15: Inspection Readiness
• Module 16: Generative AI in Non-Critical GxP Use
• Module 17: Large Language Models in Regulated Environments
• Module 18: AI in Practice — Tools You Can Use Today
• Module 19: Building the AI Programme — The First 100 Days

Part 6: Advanced Topics and Integration

• Module 20: AI and Data Integrity (ALCOA+)
• Module 21: Cybersecurity for AI Systems
• Module 22: AI in Combination Products and SaMD
• Module 23: Vendor Management for AI Systems
• Module 24: Future Trends and Regulatory Evolution

Certification Requirements

To successfully complete this training program and receive certification, you must:

Certificate of Completion

Upon successful completion of all module assessments and the final examination, you will receive a Certificate of Completion for Validating AI in GxP.

Certificate Details:

• Participant’s full name, training program title, and date of completion
• Modules completed and average score achieved
• Unique certificate number for verification purposes