Cloud-Native Data Lifecycle Governance for Trusted AI & BI
summary
A practical blueprint that fixes data-driven failures by embedding governance across four layers: secure ingestion with schema validation, encryption and provenance; continuous classification via AI/ML and cataloging; role-based access control with clear data ownership; and tiered storage (archive, cold, warm, hot) plus automated retention and auditable deletion. Phased implementation—foundation, ingestion & access, intelligence, lifecycle automation—targets multi/hybrid-cloud with code-level policies and a unified audit catalog to prove deletion and restore trust after an outage caused by bad data.
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chapters (14)
- 0:00Outage Prediction Failure
- 0:35Governance Not AI
- 1:10Session Roadmap
- 1:50Why Governance Breaks
- 4:03Governance Framework Layers
- 4:47Layer One Secure Ingestion
- 5:46Layer Two Classification
- 6:30Layer Three Access Control
- 7:18Layer Four Tiered Storage
- 9:38AI Validation Observability
- 10:40Retention And Deletion
- 11:52Implementation Phases
- 14:06Cross Cloud Consistency
- 14:53Key Takeaways Closing
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