Audit-Ready AI in Pharmacovigilance: Governance, Validation, and Documentation

This one-day advanced seminar uses a pharmacovigilance use case to demonstrate the governance, validation and risk-based control of AI across GxP-regulated environments.

• AI as a GxP-relevant component of computerized systems
• Validation strategies and lifecycle activities
• Technical guardrails, human oversight, and performance monitoring
• Risk-proportionate controls
• Practical application through workshops, industry examples, and editable templates

This training course is designed for professionals who are involved, or expect to be involved, in the compliant, controlled, and inspection-ready use of AI in pharmacovigilance. While the running case study focuses on pharmacovigilance, the governance, validation, and risk-control approach can be transferred directly to other GxP-regulated areas.

It is relevant to professionals working in pharmacovigilance and related GxP functions, including regulatory affairs, quality assurance, compliance, safety systems and digital functions, as well as consultants supporting these areas. The seminar is particularly suited to participants who

• are planning, implementing, or overseeing AI use cases.
• need to address governance, validation, and documentation requirements.
• are responsible for traceability, accountability, and audit readiness.

Basic familiarity with the key concepts, opportunities and limitations of AI is recommended. No advanced technical background is required.

The aim of this advanced training course is to provide a practical and risk-proportionate approach to the governance, validation and control of AI-supported processes in pharmacovigilance and other GxP-regulated environments.

Using a running AI-supported pharmacovigilance use case, participants follow the process from defining the intended use and validation scope to establishing appropriate controls, monitoring requirements and inspection-ready documentation. The sessions address regulatory expectations, AI subsystem validation, lifecycle activities, human oversight, technical guardrails, supplier oversight and the role of the AI Subsystem Owner.

In two practical workshops, participants apply the methods directly to the running case using the editable templates provided as part of the seminar. They define acceptance criteria, develop a test strategy and create a risk-control matrix. They also examine how relevant decisions and controls can be documented to support inspection readiness. Industry examples provide additional insight into the practical implementation of AI governance and control strategies.

The seminar follows a running case. Participants apply the concepts discussed throughout the day to an AI-supported pharmacovigilance use case, from intended use and validation through risk control and inspection-ready documentation.

The practical workshops use editable templates that are included in the seminar materials:

• AI governance RACI and decision-rights matrix
• Fit-for-purpose validation plan aligned with system type and risk class
• Risk-control matrix covering bias, hallucinations, model drift, and shadow AI
• Model card and data sheet template
• Audit-trail and documentation checklist for an AI-supported pharmacovigilance process

The templates provide a practical basis for assessing AI use cases, defining appropriate controls, and preparing the required documentation for inspection readiness.