Definition
Model governance is the framework of policies, processes, and technical controls ensuring ML systems remain compliant, accountable, and auditable—covering regulations (GDPR, CCPA, HIPAA) and traceability so any prediction can be reproduced and explained.
TECHNICAL REQUIREMENTS
Every artifact (model, dataset, feature definition) must be versioned and immutable. Every change requires documented approval and audit log entry. Every runtime decision traces back to specific model build, dataset snapshot, and exact feature values.
💡 Example: Fraud detection at 25K predictions/sec must capture request ID, model version, dataset fingerprint, feature hash, and decision explanation without violating latency SLOs.
REGULATORY STAKES
GDPR: fines up to 4% of annual global turnover. EU AI Act introduces risk classifications requiring documentation for high-risk systems. Financial Model Risk Management guidance requires seven-year retention of decision records.
MODEL CARDS
Standardized documentation of intended use, evaluation metrics, limitations, and ethical considerations. Major organizations now require responsible AI reviews and model cards for sensitive use cases before deployment.
BALANCING CONTROL AND VELOCITY
Patterns: immutable artifact registries, approval workflows with separation of duties (trainer cannot deploy), append-only audit logs, continuous monitoring for accuracy drift, data distribution shifts (PSI), and bias across protected groups.
⚠️ Goal: Every prediction traceable, every change approved, compliance checks continuous not episodic.
✓Compliance with regulations like GDPR (4% revenue fines), CCPA, HIPAA, and EU AI Act requires data privacy controls, fairness evaluations, and documented model risk management throughout the ML lifecycle
✓Auditability demands complete traceability where every prediction can be reproduced using the exact model version, dataset snapshot, and feature values from decision time, even at high throughput like 25,000 requests per second
✓Technical implementation uses immutable artifact registries with cryptographic hashes, approval workflows with separation of duties (trainer cannot deploy), and append only audit logs that cannot be tampered with
✓Production systems balance governance with performance by using asynchronous prediction journals that log metadata (request ID, model version, feature hashes) with p99 enqueue latency under 5 milliseconds to meet overall 50 millisecond SLOs
✓Model cards standardized by Google and required by Microsoft document intended use, limitations, evaluation metrics, and subgroup performance before any deployment to production environments
✓Continuous monitoring tracks data drift using Population Stability Index (PSI) with alerts when PSI exceeds 0.2 for three consecutive windows, and bias metrics across protected groups to detect harmful changes between formal reviews