Infrastructure for AI Model Governance & Monitoring

AI model governance requires explicitly documented policies defining retraining thresholds, bias monitoring criteria, performance floor metrics, and explainability standards for each deployed clinical AI model. HIPAA and emerging AI regulatory requirements mandate documentation of model validation and monitoring procedures. These policies must be current and findable — an auditor verifying governance of a sepsis prediction model needs to locate the documented drift threshold and bias monitoring methodology within minutes. Tribal knowledge about 'when models need retraining' creates unacceptable compliance risk for patient-safety AI.

AI model governance monitoring requires systematic capture of model predictions paired with actual outcomes, input data distributions, protected attribute values for bias monitoring, and performance metric time-series — through defined logging pipelines, not ad-hoc. HIPAA audit logging provides infrastructure, but model-specific outcome tracking requires defined capture processes that link predictions to subsequent ground truth events (did the sepsis alert lead to confirmed sepsis?). Template-driven capture ensures every deployed model generates the monitoring data needed for drift detection and bias assessment.

AI model governance requires formal ontology mapping models to their input features, output predictions, performance metrics, drift thresholds, bias dimensions, and regulatory requirements. This is not merely consistent schema — it requires explicit entity definitions: Model.Type (sepsis predictor), Model.InputFeatures (vital signs, lab values), Model.ProtectedAttributes (race, gender), Model.PerformanceMetric (AUC, sensitivity, specificity), Model.ThresholdTrigger (retrain if AUC drops 0.05 below baseline). Without formal relationships between these entities, the governance platform cannot automatically determine which metrics to monitor for which model or which bias dimensions are relevant to which patient population.

AI governance monitoring requires API access to model serving infrastructure (to retrieve predictions), clinical data repositories (for outcome data to compute ground truth), demographic data sources (for bias monitoring), and regulatory reporting systems. The baseline confirms monitoring tools provide API access and Active Directory integration exists. At L3, the governance platform must query model outputs in near-real-time, retrieve clinical outcomes from EHR systems for prediction-outcome pairing, and access protected attribute data for bias calculations — requiring API connectivity across clinical and technical systems.

AI governance policies, bias thresholds, and retraining criteria must update when regulatory guidance changes, patient population demographics shift, or clinical protocols are updated. Event-triggered maintenance ensures that when a new FDA guidance on AI in clinical decision support is issued, governance documentation and monitoring thresholds are updated immediately — not at the next quarterly review. Model monitoring baselines must also update after each retraining cycle to reflect the new model version's expected performance distribution.

AI model governance requires API-based connections between model serving infrastructure, clinical data repositories (EHR, lab systems), demographic data sources, alerting platforms, and regulatory documentation systems. The baseline confirms monitoring tools aggregate data and API access exists for major systems. At L3, these connections enable the governance platform to continuously compare model predictions against clinical outcomes, assess input data distribution shifts, and generate bias reports — providing closed-loop model oversight without manual data assembly.