What is Model Governance Framework?

Implement comprehensive monitoring, automated testing, version control, incident response procedures, and continuous improvement processes aligned with organizational objectives.

Build governance around six pillars: model inventory and classification (catalog all models by risk tier determining governance intensity), development standards (coding guidelines, testing requirements, documentation templates, review processes per risk tier), deployment controls (approval workflows, validation gates, change management procedures), production monitoring (performance tracking, drift detection, incident response procedures with defined SLOs per model), compliance management (regulatory mapping, audit trail maintenance, bias testing schedules, privacy impact assessments), and lifecycle management (retraining policies, retirement criteria, archival procedures). Assign a model risk owner for each production model responsible for governance compliance. Implement governance automation where possible: automated documentation generation, CI/CD policy enforcement, and monitoring-triggered reviews. Start with high-risk models and extend to lower tiers over 6-12 months.

Apply proportional governance: minimal-risk models (internal analytics, non-customer-facing) need only automated checks and documentation templates (10 minutes overhead per deployment). Medium-risk models (customer-facing recommendations, internal automation) require peer review and basic validation testing (1-2 days overhead). High-risk models (financial decisions, health-related predictions, hiring) require full governance including ethics review, bias testing, and multi-stakeholder approval (1-2 weeks overhead). Automate governance checkpoints in CI/CD pipelines so compliance is verified without manual intervention for routine cases. Use exceptions sparingly with documented justification and time-limited waivers. Review governance tier assignments annually. Track governance overhead as a metric and optimize processes that exceed target timelines without adding risk mitigation value.

Build governance around six pillars: model inventory and classification (catalog all models by risk tier determining governance intensity), development standards (coding guidelines, testing requirements, documentation templates, review processes per risk tier), deployment controls (approval workflows, validation gates, change management procedures), production monitoring (performance tracking, drift detection, incident response procedures with defined SLOs per model), compliance management (regulatory mapping, audit trail maintenance, bias testing schedules, privacy impact assessments), and lifecycle management (retraining policies, retirement criteria, archival procedures). Assign a model risk owner for each production model responsible for governance compliance. Implement governance automation where possible: automated documentation generation, CI/CD policy enforcement, and monitoring-triggered reviews. Start with high-risk models and extend to lower tiers over 6-12 months.

Apply proportional governance: minimal-risk models (internal analytics, non-customer-facing) need only automated checks and documentation templates (10 minutes overhead per deployment). Medium-risk models (customer-facing recommendations, internal automation) require peer review and basic validation testing (1-2 days overhead). High-risk models (financial decisions, health-related predictions, hiring) require full governance including ethics review, bias testing, and multi-stakeholder approval (1-2 weeks overhead). Automate governance checkpoints in CI/CD pipelines so compliance is verified without manual intervention for routine cases. Use exceptions sparingly with documented justification and time-limited waivers. Review governance tier assignments annually. Track governance overhead as a metric and optimize processes that exceed target timelines without adding risk mitigation value.

Build governance around six pillars: model inventory and classification (catalog all models by risk tier determining governance intensity), development standards (coding guidelines, testing requirements, documentation templates, review processes per risk tier), deployment controls (approval workflows, validation gates, change management procedures), production monitoring (performance tracking, drift detection, incident response procedures with defined SLOs per model), compliance management (regulatory mapping, audit trail maintenance, bias testing schedules, privacy impact assessments), and lifecycle management (retraining policies, retirement criteria, archival procedures). Assign a model risk owner for each production model responsible for governance compliance. Implement governance automation where possible: automated documentation generation, CI/CD policy enforcement, and monitoring-triggered reviews. Start with high-risk models and extend to lower tiers over 6-12 months.

Apply proportional governance: minimal-risk models (internal analytics, non-customer-facing) need only automated checks and documentation templates (10 minutes overhead per deployment). Medium-risk models (customer-facing recommendations, internal automation) require peer review and basic validation testing (1-2 days overhead). High-risk models (financial decisions, health-related predictions, hiring) require full governance including ethics review, bias testing, and multi-stakeholder approval (1-2 weeks overhead). Automate governance checkpoints in CI/CD pipelines so compliance is verified without manual intervention for routine cases. Use exceptions sparingly with documented justification and time-limited waivers. Review governance tier assignments annually. Track governance overhead as a metric and optimize processes that exceed target timelines without adding risk mitigation value.