The Problem: Traditional CSV Doesn't Fit AI/ML Systems
Traditional Computer System Validation frameworks were designed for deterministic software: given the same inputs, the system always produces the same outputs. AI/ML systems fundamentally challenge this paradigm — their outputs are probabilistic, they may evolve through continuous learning, and their decision logic is often not fully interpretable. Standard GAMP 5 categories don't cleanly accommodate these characteristics, leaving organizations either over-engineering validation or dangerously under-validating.
The Solution: A Risk-Based AI Validation Framework
We classify AI/ML systems along two dimensions: GxP impact (patient safety, data integrity, business process support) and model complexity (rule-based through to generative AI). This classification matrix determines appropriate validation rigor, avoiding both excessive bureaucracy and insufficient controls.
The Approach: Continuous Assurance Replaces Point-in-Time Validation
- Intended Use Documentation: Precise definition of what the AI system does, including performance boundaries, known limitations, and failure modes
- Training Data Governance: Provenance, quality, representativeness, and bias assessment of training datasets
- Continuous Performance Monitoring: Real-time dashboards tracking accuracy, drift, and distribution shift — replacing point-in-time validation with continuous assurance
- Change Control for Model Updates: Formal processes for evaluating, testing, and approving retraining and architecture changes
- Explainability Documentation: Appropriate to model type and risk level
This framework aligns with EMA's reflection paper on AI, Swissmedic guidance, and FDA's TPLC approach for AI/ML-based SaMD.
How ANG Associates Can Help
ANG Associates has deep expertise in both traditional CSV/GAMP 5 validation and modern AI/ML systems. We help pharma organizations design and implement validation frameworks that satisfy regulators while remaining practical for development teams. Our approach bridges the gap between quality assurance teams (who understand validation) and data science teams (who understand AI) — creating shared frameworks both groups can work with effectively.