Regulated organisations are deploying AI faster than their governance frameworks can keep pace. The validation questions this creates are not new. But applying them to systems that learn, adapt and change without a code release requires specific expertise. Senior, independent AI governance support from first risk assessment to inspection-ready evidence.
AI introduces dynamic behaviour, opaque decision pathways and novel failure modes. The existing GAMP 5 and CSA frameworks provide a foundation. Applying them to systems that learn, adapt and change without a code release requires specific expertise.
FDA, EMA and MHRA are all actively developing AI guidance — including EMA's draft EU GMP Annex 22 on AI in GMP-regulated manufacturing, currently out for stakeholder and expert consultation, and the EU AI Act's broader risk-based framework, whose high-risk provisions may extend to AI used in regulated manufacturing and quality processes. Organisations deploying AI in GxP workflows now are setting their own inspection precedents. Getting governance right early is significantly less expensive than remediating it under inspection pressure.
Intended use definition, patient and product impact analysis, system categorisation and control rationale. The documented foundation regulators look for first.
A defensible assurance approach proportionate to risk and intended use, covering model behaviour, human oversight requirements and performance acceptance criteria.
Formal governance for model updates, prompt and configuration changes, and training data changes, with impact assessment templates and approval records.
ALCOA+ gap assessment for AI-generated records: provenance traceability, audit trail design for model outputs, and input data governance.
Drift detection controls, periodic review cadence, defined thresholds and documented escalation paths. Demonstrates ongoing governance across the model lifecycle.
Qualification approach for AI platform and model vendors: shared responsibility matrix, change notification expectations and evidence of vendor quality system maturity.
The regulatory frameworks for AI in regulated environments are actively developing. FDA, EMA and MHRA have all published or signalled guidance. Organisations that understand the direction of travel can build governance that holds, rather than retrofitting it after the first inspection observation.
AI governance in GxP environments encompasses a broad range of system types, from traditional algorithmic decision support to modern large language model integrations. The regulatory principles apply across all of them.
A focused 30-minute call to understand your systems, your current controls and where inspection risk is highest. No commitment. Direct senior expertise from the first conversation.
The validation framework that AI governance sits within: risk-based, GAMP 5-aligned, inspection-defensible.
→ DIALCOA+ controls for AI-generated records: provenance, audit trail design and input data governance.
→ SaaSSupplier qualification and change governance for AI platform vendors in regulated environments.
→