Categorize governance obligations across the AI lifecycle.
Sort each governance obligation into the lifecycle bucket where it primarily creates control. Data Model Deployment Monitoring Confirm data source permission and allowed processing purpose Remove fields not needed for the model objective Document training and evaluation data provenance Evaluate false positives and false negatives by relevant slice Write known limitations and out-of-scope uses Stress-test model behavior on edge-case inputs Define who can override AI output before action Set rollback trigger and accountable incident owner Give users notice when AI materially shapes the workflow Track drift, override rates, complaints, and incident patterns
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