The rails GenAI needs to be trusted at scale.
Policies, model registry, use-case intake, risk classification, HITL policy, red-teaming and drift — designed for regulated environments.
AI governance is the operating model deciding which AI use cases are permitted, who approves them, what evidence each needs before production, and how they are monitored afterwards. It sits above the validation of any individual system: validation proves one system is fit for use, governance decides which systems should exist and keeps the inventory.
Outcomes you can expect
- Approved policy pack
- Model registry live
- Use-case intake operating
AI Governance is delivered by our ai transformation practice. Where it saves time, the team uses our products: GxP Copilot for validation lifecycle work and TraceDraft for clinical documentation.
Explore related capabilities: AI Strategy, AI Validation, GenAI Adoption, AI Centre of Excellence (AI CoE).
AI Governance in Regulated Life Sciences: The Six Requirements
AI governance in a regulated life sciences organisation is not optional infrastructure — it is the precondition for responsible AI adoption. Without a governance framework, AI tools proliferate in uncontrolled ways: individual teams deploy AI tools for GxP-relevant tasks without quality review; vendors change underlying models without notification; AI-generated content enters regulated documents without traceability to the model version that generated it; and the organisation cannot demonstrate to a regulatory inspector which AI systems are in use, what they are approved for, and how their outputs are controlled.
The six requirements of an effective AI governance framework for regulated environments are: a use-case intake process that evaluates new AI proposals for GxP impact before deployment; a risk classification system that assigns each AI use case to the appropriate oversight tier; an AI model registry that tracks every AI system in production use, its approved scope, its validation status, and its change history; a human-in-the-loop policy that specifies which AI outputs require human review and who is qualified to provide that review; a drift monitoring programme that detects performance degradation; and a policy framework that governs acceptable AI use across the organisation.
The AI Model Registry: Operational Foundation of Governance
The AI model registry is the operational foundation of the AI governance framework — the single source of truth for every AI system in production use. Each registry entry documents: the AI system name and vendor; the model version and training data basis; the approved scope of use (what the AI is approved to do, and what it is not approved to do); the GxP risk classification; the validation status and reference to the validation documentation; the human oversight policy for this system; the change history; and the last performance benchmark result.
The registry enables the governance function to answer the questions that a regulatory inspector or senior leader might ask: what AI systems do we use in our quality processes? What is each system approved for? Has each system been validated? Who is responsible for each system's compliance? When was each system's performance last assessed? Without the registry, these questions require surveying individual teams and reconstructing information that was never systematically captured. With the registry, these answers are available on demand.
Red-Teaming and Adversarial Testing for Regulated AI
Red-teaming — deliberately attempting to produce incorrect, harmful, or non-compliant outputs from an AI system — is the most valuable quality assurance technique for GenAI systems that is consistently absent from AI governance frameworks. For an AI system used in a regulated context, the relevant red-team scenarios are not primarily adversarial user attacks — they are the domain-specific failure modes that could produce incorrect regulatory conclusions, patient safety errors, or non-compliant documentation.
For a GenAI system used to assist in safety narrative drafting, the relevant red-team scenarios include: does the system hallucinate clinical events not in the source data? Does it misattribute AE causality? Does it generate plausible-sounding narrative text for an AE that does not exist in the AE listing? For a GenAI system used in CAPA root cause analysis support, the red-team scenarios include: does the system systematically underweight certain root cause categories? Does it generate root cause statements that are grammatically correct but logically unfalsifiable? Red-teaming documentation is evidence that the organisation understands the failure modes of its AI systems and has tested them — the kind of documentation that distinguishes a mature AI governance programme from an aspirational one.
See it on your own system.
Bring a URS or a system. We show you the validation package it produces.
- We reply within one business day
- We run your URS live on the call
- You get a clear plan and scope