Building a Model Governance Platform for Regulated Industries

When you have 5 models, governance is a spreadsheet. When you have 500, it's a platform engineering problem. Regulated enterprises deploying AI at scale need automated model governance — not more committees.

What Model Governance Requires

• Model inventory — Every model cataloged with owner, purpose, risk tier, regulatory scope, and deployment status
• Lifecycle management — From experiment to production to retirement, with approval gates at each stage
• Validation workflows — Automated and human validation steps based on model risk classification
• Monitoring and alerting — Drift detection, performance degradation, fairness metrics, data quality
• Audit trail — Every decision, change, and approval documented immutably

The Model Registry as Governance Hub

The model registry isn't just a place to store model artifacts. In a governance context, it's the system of record for every model's compliance status. Each model version links to: its training data lineage, validation results, risk assessment, regulatory mapping, approved serving configuration, and monitoring thresholds.

Automated Compliance Checks

Build compliance checks into the CI/CD pipeline for models:

• Pre-training: Data consent verification, bias assessment of training data
• Post-training: Fairness metrics across protected groups, explainability score, robustness testing
• Pre-deployment: Model card completeness, risk tier approval, serving configuration validation
• Post-deployment: Continuous monitoring against governance thresholds

Policy-as-Code for AI

Express governance policies as code (OPA, Kyverno) and enforce them at the infrastructure level. "No model above risk tier 2 can be deployed without independent validation" becomes a policy that the platform enforces automatically — not a rule that someone might forget to check.