Module 6: Governing AI Development, BoK III.B
Governing the AI Data Life Cycle
Data governance spans ingestion to decommissioning with cross-functional stewardship. The data life cycle runs Collection → Use → Disclosure → Retention → Destruction, the last making personal data unrecoverable.
Data governance does not stop at collection → it spans ingestion to decommissioning, with cross-functional stewardship shared by privacy, risk, ML, legal and governance teams.
The data life cycle
Collection (gathering data about an individual) → Use → Disclosure (sharing or providing access) → Retention (saving until destruction) → Destruction (making personal data unrecoverable).
Five oversight areas:
- Training data governance → validate lawful basis, data minimisation, accuracy and diversity during training → assess bias risks → maintain reproducibility logs
- Evaluation & testing → govern validation and test sets → fairness metrics, drift testing, edge case analysis → embed explainability obligations early
- Deployment → policies for real-time data inputs, human-in-the-loop models and retraining triggers → enforce access controls and logging
- Monitoring & drift detection → continuously audit inputs and outputs for data drift, concept drift and changes in quality or representativeness → flag anomalies for governance review
- Decommissioning → secure deletion or archiving of datasets, training artifacts and output logs per regulatory retention policies → document rationales and impacts
Key terms - quick answers
What is “Data life cycle”?
Collection, Use, Disclosure, Retention, Destruction - the stages personal data passes through.
What is “Destruction”?
Making personal data unrecoverable at the end of the data life cycle.
Sources and study method
This independent lesson uses active recall, spaced retrieval and scenario practice. Read the full study method.