Validating AI Use in Quality Systems
Practical CSV and CSA Approaches for FDA Inspection Readiness
- Limitations of traditional CSV for AI-assisted GxP systems
- Applying CSA principles to non-deterministic software behaviors
- Defining intended use and operational AI boundaries
- Risk classification based on quality and data integrity impact
- Verification expectations for AI-generated outputs and decisions
- Maintaining human accountability and review responsibility
- Documentation and traceability aligned with ALCOA++ expectations
Organizations introducing AI into GxP quality systems must decide how validation, oversight, and accountability will function when outputs are variable and context-dependent.
This session structures practical validation approaches that combine traditional CSV discipline with CSA principles, risk classification, verification controls, and operational accountability for AI-assisted workflows.
The session begins by examining where conventional validation approaches lose effectiveness with non-deterministic systems and how CSA thinking changes validation priorities. It then progresses into intended-use definition, risk-based classification, verification expectations, accountability structures, and inspection-focused documentation practices that support controlled AI implementation within operational quality systems.
Carolyn Troiano has more than 30 years of experience in computer system validation across pharmaceutical, medical device, and other FDA-regulated industries. She helped develop validation strategies during the early evolution of FDA computer system guidance and collaborated directly with FDA and industry groups on 21 CFR Part 11, giving her practical perspective on applying validation discipline to emerging technologies.