Using EU GMP Annex 22 as the Framework for Governing AI Systems, Validation Traceability, and Data Integrity Controls

Artificial Intelligence is transforming how regulated organizations manage quality systems, manufacturing operations, compliance activities, and business processes. AI-enabled technologies now support activities ranging from document generation and data analysis to predictive maintenance, process optimization, quality investigations, and decision support. As adoption accelerates, regulatory expectations regarding governance, validation, traceability, and data integrity continue to evolve.

This webinar examines the practical implications of EU GMP expectations as they relate to AI systems operating within regulated environments. Participants will explore how traditional GMP principles intersect with emerging AI technologies and how organizations can establish governance structures that maintain compliance while supporting innovation.

The session focuses on the unique challenges associated with validating AI-enabled systems. Unlike traditional software applications, AI models may demonstrate adaptive behavior, rely on large training datasets, generate probabilistic outputs, and require ongoing monitoring throughout their operational lifecycle. Participants will learn how validation traceability can be established and maintained despite these complexities and how organizations can create evidence demonstrating continued fitness for intended use.

Special attention will be given to data integrity considerations, including training data governance, source data reliability, auditability, model transparency, output verification, electronic records management, and change control requirements. Participants will examine how AI-related risks can affect GMP decision making and how effective oversight mechanisms can reduce potential compliance vulnerabilities.

The webinar will also address governance structures necessary to support AI implementation, including roles and responsibilities, lifecycle management practices, risk assessment methodologies, performance monitoring programs, retraining controls, and inspection readiness activities. Real-world implementation challenges associated with integrating AI technologies into existing quality systems will be discussed throughout the session.

By the conclusion of the webinar, attendees will understand how GMP expectations apply to AI-enabled systems, how to establish defensible validation and governance frameworks, and how to maintain traceability and data integrity controls capable of supporting both regulatory compliance and sustainable operational performance.

_{WHY YOU SHOULD ATTEND:}

Many organizations are currently evaluating or implementing AI technologies without fully understanding how existing GMP requirements apply to these systems. While AI offers tremendous opportunities to improve operational efficiency, decision support, investigation effectiveness, process monitoring, and knowledge management, it also introduces risks that can create significant regulatory concerns if not properly controlled.

Regulators are increasingly focused on how companies govern AI systems that influence GMP decisions. Questions regarding model validation, data integrity, algorithm transparency, audit trail capabilities, change management, retraining activities, performance monitoring, and human oversight are becoming important components of regulatory discussions. Organizations that cannot adequately explain how AI-generated outputs are created, verified, controlled, and maintained may face increasing scrutiny during inspections.

A common misconception is that AI systems can simply be validated using traditional software validation approaches. In reality, many AI technologies require additional considerations involving training datasets, model drift, performance degradation, continuous learning mechanisms, output verification processes, and lifecycle governance. Failure to address these issues can create compliance risks that extend beyond technology implementation and directly affect product quality, patient safety, and regulatory defensibility.

This webinar provides practical guidance for understanding how GMP principles apply to AI-enabled systems and how organizations can establish governance frameworks that support compliance while enabling innovation. Participants will learn how to build validation strategies, maintain traceability, strengthen data integrity controls, establish oversight mechanisms, and prepare for increasing regulatory attention surrounding AI technologies. Whether evaluating AI for future implementation or managing existing deployments, attendees will gain practical insights that support both compliance and operational success.

_{AREAS COVERED:}

• Introduction to AI within regulated GMP environments 
• Emerging EU regulatory expectations for AI governance 
• AI system classification and risk-based oversight 
• Validation challenges unique to AI-enabled systems 
• Establishing validation traceability requirements 
• AI lifecycle management and governance frameworks 
• Training data governance and control strategies 
• Data integrity implications of AI technologies 
• Model transparency and explainability considerations 
• Audit trails and electronic record requirements 
• Human oversight and decision accountability 
• AI-generated content verification practices 
• Change management for AI systems 
• Model retraining and performance monitoring 
• Managing model drift and degradation risks 
• Risk assessment methodologies for AI applications 
• Supplier qualification and third-party AI oversight 
• Inspection readiness for AI-enabled processes 
• Documentation expectations for AI validation 
• Future regulatory trends impacting AI governance

_{WHO SHOULD ATTEND:}

• Quality Assurance Directors 
• Quality Systems Managers 
• GMP Compliance Professionals 
• Validation Managers 
• Computer System Validation Specialists 
• Digital Transformation Leaders 
• Manufacturing Directors 
• Operations Directors 
• Regulatory Affairs Professionals 
• Data Integrity Specialists 
• IT Quality Managers 
• Automation Engineers 
• Process Engineers 
• Quality Risk Management Leaders 
• Internal Auditors 
• Supplier Quality Professionals 
• Technical Services Personnel 
• AI Governance Teams 
• Compliance Consultants 
• Executive Leadership Teams

 _{TOPIC BACKGROUND}

Artificial Intelligence is rapidly becoming integrated into regulated pharmaceutical, biotechnology, medical device, and advanced therapy environments. Organizations are deploying AI-enabled technologies to support manufacturing operations, quality systems, deviation investigations, predictive maintenance, process monitoring, document generation, supplier management, quality risk assessments, and regulatory compliance activities. While these technologies offer significant opportunities for efficiency and decision support, they also introduce new challenges related to governance, validation, traceability, accountability, and data integrity.

European regulators have increasingly recognized that traditional computerized system validation approaches may not fully address the unique characteristics of AI-enabled systems. Unlike conventional software applications that generally produce predictable outputs based upon fixed programming logic, AI systems may learn, adapt, evolve, and generate outputs that can change over time. These characteristics create new expectations regarding lifecycle management, validation methodologies, model monitoring, data governance, auditability, and oversight.

Organizations adopting AI technologies must demonstrate that decisions supported by AI systems remain scientifically justified, traceable, explainable where required, and compliant with GMP expectations. Regulators are placing growing emphasis on understanding how AI systems are governed, how training data is controlled, how outputs are verified, and how organizations maintain confidence in system performance throughout the operational lifecycle.

This webinar explores the emerging expectations associated with AI governance within regulated environments, focusing on validation traceability, lifecycle management, data integrity controls, risk management, and inspection readiness considerations relevant to EU GMP operations.

Course Director: CHARLES H. PAUL

Charles H. Paul is President of CHP Consulting LLC, a regulatory compliance, operational excellence, technical training, and documentation consulting firm serving regulated industries worldwide. With more than 30 years of experience supporting pharmaceutical, biotechnology, medical device, and advanced therapy organizations, Charles specializes in helping companies design sustainable systems that improve compliance, workforce performance, operational effectiveness, and inspection readiness.

His experience includes quality systems implementation, GMP compliance, validation programs, technical documentation, training system development, computerized systems oversight, operational excellence initiatives, risk management, and regulatory remediation activities. Throughout his career, Charles has worked with both emerging organizations and Fortune 500 companies to develop practical solutions that align regulatory expectations with operational realities.

As artificial intelligence becomes increasingly integrated into regulated environments, Charles focuses on helping organizations understand how traditional GMP principles apply to emerging technologies. His practical approach emphasizes governance, risk management, validation strategy, operational control, and sustainable compliance rather than technology adoption alone.

Charles is a recognized speaker, consultant, and author who has delivered numerous webinars and seminars on GMP compliance, inspection readiness, quality systems, operational excellence, technical writing, human performance improvement, validation, and regulatory risk management. His presentations translate complex regulatory concepts into practical implementation strategies that organizations can apply immediately to improve both compliance and business performance.