AI models aren't set-and-forget. They need governance from development to retirement. Here's what that actually means.
The Model Lifecycle
Every AI model goes through phases. Governance means controls at each one:
| Phase | Governance Focus |
|---|---|
| Development | Standards, documentation, testing |
| Validation | Performance checks, bias testing |
| Deployment | Approval process, rollback plan |
| Monitoring | Drift detection, performance tracking |
| Retirement | Sunset process, replacement |
What Model Governance Includes
Development Standards
- Data requirements: What data can be used, how it's sourced
- Documentation: Model cards, training data records
- Version control: Tracking changes to models and data
- Testing requirements: What tests must pass before deployment
Validation Process
- Performance benchmarks: Accuracy, speed, reliability
- Bias testing: Fairness across demographic groups
- Edge cases: How model handles unusual inputs
- Security review: Vulnerabilities, attack vectors
Deployment Controls
- Approval workflow: Who signs off on production deployment
- Rollback plan: How to revert if issues emerge
- Integration testing: Works with existing systems
- User communication: Notifying affected teams
Ongoing Monitoring
- Model drift: Performance degrading over time
- Data drift: Input data changing from training data
- Usage patterns: How the model is actually being used
- Error rates: Failures, edge cases, exceptions
Retirement Process
- Trigger criteria: When to retire a model
- Replacement: Transition to new model
- Documentation: Archive for compliance
- Communication: Notify users of change
Who Owns Model Governance?
| Role | Responsibility |
|---|---|
| Data Science Team | Technical implementation, testing |
| Business Owner | Use case decisions, risk acceptance |
| Compliance/Legal | Regulatory requirements, risk review |
| IT/Operations | Deployment, monitoring infrastructure |
| Governance Council | Policy, escalated decisions |
Model Documentation Requirements
Every model should have:
- Model card: Purpose, limitations, performance
- Training data record: Sources, preprocessing, bias checks
- Test results: What was tested, outcomes
- Approval trail: Who approved what, when
- Monitoring plan: What metrics, what thresholds
- Retirement criteria: When to replace
Regulatory Considerations
Depending on industry and location:
- EU AI Act: Risk classification, transparency requirements
- Industry regulations: Healthcare, finance, etc.
- Data privacy: GDPR, CCPA, training data consent
- Audit requirements: Documentation for examination
Signs You Need Better Model Governance
- Models deployed without documentation
- No one knows which models are in production
- Model failures surprise the team
- No process to retire outdated models
- Bias or fairness issues discovered too late
- Regulators asking questions you can't answer
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