AI/ML Governance and Validation in Regulated GxP Environments
Governing AI and machine learning systems in regulated pharmaceutical, biotech, and medical device environments cannot be reduced to writing a policy or adopting a generic framework. It is an engineering discipline.
Why AI Governance is an Engineering Issue, Not Just a Policy Matter
An AI/ML system in a regulated environment differs fundamentally from a traditional GxP computerised system. A deterministic system always produces the same output given the same inputs. An AI/ML system produces probabilistic outputs that may vary based on training data, model parameters, and the distribution of production inputs relative to the training dataset.
The Regulatory Framework
GMP Annex 22 is the first formal European GMP normative response to AI in pharmaceutical environments. Core principles include: documented and approved Intended Use; mandatory human oversight for high-impact GMP decisions; formal lifecycle management; change management for model updates and retraining; and production monitoring against validation-defined acceptance criteria.
EU AI Act Annex III classifies as high-risk a range of AI system categories including healthcare applications. Requirements include a dedicated AI QMS, fundamental rights risk assessment, complete technical documentation, EU database registration, automatic operational logging, and designated technical responsible persons.
GAMP 5 Second Edition introduces AI/ML software as an autonomous category requiring risk-based application with statistical performance metrics, enhanced vendor assessment, and periodic reviews including model drift monitoring.
Deterministic vs Probabilistic Systems: Why the Difference Is Critical in GMP
Unlike deterministic systems (ERP, LIMS, MES), ML models can change behaviour without formal code modifications — simply because production data diverges from the training set. This requires continuous performance monitoring, more complex change control, and audit trails that capture input, output, and model context for every relevant decision.
LLMs in Critical GMP Contexts: Large Language Models present a particularly complex case. Their non-deterministic, prompt-dependent behaviour and susceptibility to hallucinations require specific risk analysis and, in most cases, a robust human-in-the-loop design with mandatory review of every output before use in production.
Model Validation Lifecycle
Intended Use and User Requirements must be formally documented before any development or vendor selection. A well-defined Intended Use specifies the supported GxP process, accepted input types, produced output types, autonomy boundaries, model acceptance criteria, and operability conditions.
Change Control for AI Models requires formal management of categories that do not exist for traditional systems: retraining on new data, algorithm or weight updates, training dataset changes, and classification threshold modifications. Model drift monitoring must be defined at validation time and implemented as a formal process.
Explainability, Traceability, and Bias Assessment
Explainability must be implemented with specific, documented, and verifiable methods (SHAP, LIME, attention mechanisms, counterfactual explanations). Bias assessment must include training set representativeness analysis, disaggregated performance testing, documentation of low-performance areas, and periodic review against production data.
Our Operational Approach
Dalia IA supports organisations through a modular AI governance service including: AI Governance Gap Analysis; AI Policy Framework drafting; Model Validation Planning; Intended Use and URS support; AI Change Control Framework design; Bias Assessment; and Inspection Readiness Review.
Each deliverable is audit-ready and structured to support GMP, EU AI Act, and sector-specific regulatory inspections.
Domande frequenti
What is the difference between AI validation and traditional computer system validation?
AI system validation differs from traditional CSV in the probabilistic nature of the validated system. A deterministic system can be validated with acceptance tests. An AI/ML system requires statistical performance testing, continuous production monitoring, and a change control process specific to events such as retraining.
What does the EU AI Act require of pharmaceutical companies using AI in GMP processes?
Companies using high-risk AI systems (EU AI Act Annex III) must implement an AI-specific quality management system, conduct a risk assessment including fundamental rights risks, ensure transparency and complete technical documentation, register the system in the EU database, and maintain automatic system logs.
How does GMP Annex 22 integrate with existing validation frameworks (GAMP 5, Annex 11)?
Annex 22 does not replace Annex 11 or GAMP 5: it completes them. The operational framework is an integration of all three, with Annex 22 adding AI/ML-specific principles: lifecycle management, model change control, explainability, and human oversight.
Can an LLM be used in a critical GMP process?
Using LLMs in critical GMP processes requires a very rigorous approach. Non-deterministic behaviour and the hallucination risk require robust human-in-the-loop design, a precise Intended Use definition, and validation documentation demonstrating risk control.
What is model drift and why is it relevant in GxP environments?
Model drift is the phenomenon by which an AI/ML model that was validated at deployment sees its performance degrade over time because production data changes its distribution relative to the training dataset. In a GxP environment, model drift may jeopardise the system's validated state.