Integrating AI Governance into Existing Workflows

This guide shows how to integrate governance into daily operations using structured workflows, automated checks, and clear approval gates.

1. Start Governance at Use Case Intake

Early governance ensures models enter the lifecycle with clear context and risk classification.

Best Practices:

• Trigger governance when a model or use case is created.
• Open a tracking ticket in your workflow tool (e.g., Jira).
• Assign an initial risk level based on business impact.
• Route models by implementation type: default code, SageMaker, LLM, or vendor.

Example: A credit risk model enters as a “high risk” use case and is routed into a more rigorous approval and monitoring path.

2. Define Required Assets Upfront

Governance is only as strong as the documentation and artifacts behind it.

Minimum Required Assets:

• Readme file
• Input schema and output schema
• Training data
• Baseline data and comparator data
• Metrics test configuration and DMN criteria

LLM-Specific Assets:

• RAILS test questions
• RAILS file
• Test data

Best Practice: Block progression in the workflow until all required assets are present.

3. Automate Standard Risk Tests

Automated risk testing ensures consistency across models and reduces human error.

Steps:

1. Run the standard risk test suite on each model snapshot.
2. Persist results in your governance system.
3. If tests fail, create a ticket with failure details.
4. Allow re-tests once the ticket is moved to “Done.”

Example: For a regression model, tests might check performance stability, data drift, and concept drift before deployment.

4. Enforce Stage-Specific Approvals

Governance gates ensure that only validated models progress to production.

Stages and Gates:

• Validation Approval: Confirms readiness for pre-production testing.
• Production Approval: Final clearance before go-live.
• Documentation: Store reviewer, decision, timestamp, and artifact references.

Tip: Use clear stage tags like DEV, SIT, UAT, and PROD for easy tracking.

5. Align with Runtime Environments

Match deployment patterns to business and technical needs.

Deployment Types:

• Batch (Ephemeral): Runs a job and frees the runtime afterward.
• Online (Persistent): Maintains live endpoints for REST or streaming.

Endpoint Examples:

• REST: Synchronous API calls for real-time scoring.
• Kafka: Topic-based message streaming for high-throughput predictions.

6. Snapshot Before Deployment

Snapshots freeze the state of a model, ensuring traceability and reproducibility.

Best Practices:

• Create a snapshot for each deployment.
• Include code, data, configurations, and test results.
• Mark key lifecycle states such as “Deployed to Validation” and “Deployed to Production.”

7. Add Monitors Before Go-Live

Monitoring ensures that models remain reliable after deployment.

Default Monitors:

• Performance monitor
• Data drift monitor
• Concept drift monitor
• Stability monitor (PSI/CSI)

Tip: Choose monitors based on methodology (classification vs. regression) and auto-attach them before production approval.

8. Maintain Consistency with Back Tests and Batch Jobs

Governance does not end at deployment—periodic re-checks are key.

Back Test Practices:

• Use fresh labeled data to validate ongoing performance.
• Apply the same pass/fail logic from initial approval.
• Create tickets for failures with full inputs, outputs, and errors.

Batch Job Governance:

• Tag batch models for easy targeting.
• Log all execution details for audit purposes.

9. Run Annual Reviews on Time

Annual reviews keep models in compliance and address changes in risk.

Best Practices:

• Trigger reviews 30 days before snapshot expiration.
• Re-run standard risk tests.
• Have a senior analyst sign off on results.
• Update metadata with validator info and new expiration date.

10. Plan for Errors and Retries

Error handling is a governance feature, not an afterthought.

Principles:

• Treat every error as an event to log and track.
• Provide context for quick resolution.
• Allow steps to be re-run without creating data or state inconsistencies.

11. Apply Governance to Vendor and Managed-Service Models

Vendor-provided models still require governance.

Best Practices:

• Require full documentation before review.
• Run an independent risk assessment.
• Apply the same production approval process as in-house models.

Governance Workflow Checklist

Intake:

• Trigger on model creation
• Create business approval ticket
• Assign initial risk rating
• Route by implementation type

Assets:

• All required artifacts present
• LLM-specific assets for LLM models

Pre-Deployment:

• Snapshot created
• Risk tests passed
• Monitors attached
• Validation and production approvals complete

Annual Review:

• Trigger before expiration
• Risk tests re-run
• Document approved and signed off
• Metadata updated

Common Pitfalls to Avoid

• Missing assets delay approvals. Fix: Enforce asset checks before tests.
• Untracked approvals create gaps. Fix: Require tickets with explicit decisions.
• No monitoring before go-live. Fix: Auto-attach monitors based on methodology.
• Expired snapshots remain in production. Fix: Automate annual review triggers.

Conclusion

Integrating AI governance into existing workflows is about automation, clear gates, and consistent documentation. By aligning governance with the tools teams already use—like Jira—and automating repeatable steps such as asset checks, risk tests, and stage approvals, organizations can maintain compliance without slowing delivery. The result is a governance framework that scales with your AI initiatives and supports reliable, auditable, and ethical model operations.

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