--- title: "A 7-Phase Framework for AI/ML Compliance in Life Sciences" description: "Discover a 7-phase AI/ML compliance framework for life sciences, covering data governance, validation, and post-launch monitoring under GxP." url: https://qbdgroup.com/en/blog/ai-compliance-framework-life-sciences type: "Blog post" language: en published: 2026-04-29 author: "Jonathan Boel" category: "Software & Digital Health" publisher: "QbD Group" citation: "QbD Group, \"A 7-Phase Framework for AI/ML Compliance in Life Sciences\", https://qbdgroup.com/en/blog/ai-compliance-framework-life-sciences" --- # A 7-Phase Framework for AI/ML Compliance in Life Sciences > Discover a 7-phase AI/ML compliance framework for life sciences, covering data governance, validation, and post-launch monitoring under GxP. One of the most common questions I hear from life sciences companies starting an AI initiative is simple: where does compliance fit in? My answer is always the same: everywhere — from the very first scoping conversation to post-launch monitoring. At QbD Group, together with our partner delaware, we have developed an AI/ML compliance framework that governs each stage of the AI lifecycle. It brings together two core building blocks: - **Data governance**, controlling the data that goes into your models - **AI/ML compliance**, covering the verification and validation of the models themselves Together, they form the backbone of compliant AI in regulated environments. ## Phase 1: Define Intended Use and Data Governance Everything starts here, and this step defines everything that follows. Before any development begins, organizations must define the performance characteristics they are aiming for: accuracy, precision, robustness, operational performance, and bias thresholds. These should be grounded in concrete benchmarks. If your AI model is meant to replace an existing process, it should at least match or exceed current performance. At the same time, a formal data governance plan must be established. This includes data ownership, access controls, retention policies, and full traceability of datasets. It should be clear upfront what your data goes through: preprocessing steps, labelling criteria, and splitting rules. This process must be documented and approved before model training starts. We often refer to this as the **process-first principle**. ## Phases 2 to 4: Data Acquisition, Preparation, and Model Training The technical design phases involve rapid iteration on dataset preparation, model selection, and testing. In a **GxP** context, a key principle is to start with static, deterministic models. Outside of GxP, there is more flexibility to explore dynamic or probabilistic approaches. Another critical principle is **human-in-the-loop design**, integrated directly into the technical setup. This is not only relevant for compliance, but also for adoption and trust across the organization. In parallel, data quality and bias assessment must be performed. Based on the intended use, organizations need to evaluate the representativeness and completeness of their data and identify potential sources of bias. This is where pharma's existing strengths become clear: organizations with mature data governance already understand provenance, lineage, and version control. ## Phases 5 and 6: Model Verification and Validation These phases answer two distinct questions. - **Verification:** did we build it right? This includes installation testing, functional testing, unit testing, integration testing, and full system testing — all traceable to software requirements. - **Validation:** did we build the right thing? This is your user acceptance testing stage, confirming that the model supports the intended GxP decisions in a real-world context. A common approach is a **soft launch**, where the AI model runs alongside the existing process before going fully live. ## Phase 7: Post-Launch Monitoring This is often the most underestimated phase, and the one that clearly distinguishes AI from traditional software. AI models carry inherent risks such as **model drift** and performance degradation, making continuous oversight essential. This includes: - Performance monitoring against predefined thresholds - Incident and near-miss reporting - Model drift detection - Periodic revalidation - Change management processes If the intended use expands over time, a proper impact assessment is required. The original training data may no longer be representative, and this is where bias can silently emerge. ## A Cycle Proven in MedTech, Ready for Pharma This lifecycle approach has already been applied successfully in MedTech for several years. The framework combines: - Strong data governance - Structured AI/ML compliance - Integration with traditional validation and post-market surveillance Together, these elements ensure that AI systems remain compliant with **GxP** regulations. And in practice, combining QbD Group's compliance expertise with delaware's digital capabilities ensures that regulatory and technical implementation are addressed as one coherent effort. ## Ready to Implement a Structured AI Compliance Approach? At QbD Group, we support life sciences companies in implementing AI frameworks that are both compliant and scalable — from initial scoping to post-launch monitoring. --- Source: https://qbdgroup.com/en/blog/ai-compliance-framework-life-sciences — © QbD Group. Quote freely with attribution and a link back.