Insight Brief · AI & Governance

AI governance in regulated systems:
making AI inspection-defensible.

AI-enabled features introduce dynamic behaviour, opaque decision pathways and new failure modes. Regulated organisations must ensure the same fundamentals apply: traceability, control and evidence of performance.

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Governance Controls

AI controls to
implement early.

These controls form the core of a governance framework that can demonstrate AI is managed proportionately and defensibly, before an inspector asks.

01
Intended use and risk classificationDefine what the model does and what decisions it influences. This determines the scope and depth of governance required and justifies all downstream control decisions.
02
Model version controlTreat model changes as controlled changes. Record versions, deployment dates and impact assessments for every change, including prompt and configuration changes.
03
Training data governanceProvenance, quality checks and change control for datasets. Data quality issues in training data compound under inspection scrutiny.
04
Performance monitoringDrift detection, periodic review, documented thresholds and actions. AI systems degrade over time, and monitoring demonstrates active, ongoing governance.
Evidence Strategy

Inspection-ready
evidence.

An AI evidence set must demonstrate both that the system was appropriately assured at deployment and that it remains controlled in operation.

01
Risk assessmentDocumenting model use, patient or product impact, decision pathways and mitigations. This is the foundation for all other governance decisions and the starting point for inspection.
02
Change control recordsFor model updates, prompt changes, configuration changes and training data changes. Each must have an impact assessment, approval and outcome record.
03
Assurance evidenceValidation or assurance evidence for key decision pathways, proportionate to risk and intended use of the AI functionality.
04
Monitoring outputsDrift detection records, periodic review outputs and documented actions taken on anomalies or threshold breaches, demonstrating the system remains in a controlled state.
Common Questions

Frequently asked.

Do we need to validate AI like traditional software?
Appropriate to risk, with additional controls
AI typically requires additional controls for model changes, drift and monitoring alongside traditional software controls. The assurance approach must be justifiable and proportionate to the risk of the intended use.
What is the biggest AI governance pitfall?
Uncontrolled change
Model updates, prompt changes or training data changes occurring without formal impact assessment and evidence. Regulators treat these as controlled changes regardless of how minor they appear.
How do we make AI defensible under inspection?
Document, control, monitor, evidence
Document intended use, implement version control and monitoring, define human oversight requirements, and retain evidence of performance and governance decisions across the system lifecycle.

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