Episode 87 — Align data governance to analytics and AI needs without losing control (1C1)

This episode explains how to align data governance to analytics and AI needs so the enterprise can increase insight and automation without losing control over privacy, quality, lineage, and accountability. You’ll learn how analytics and AI expand risk surfaces through broader data access, more data copies, new derived datasets, and model-driven decisions that can amplify data quality problems, bias, or misuse. We’ll cover governance requirements that enable safe scale, including clear data ownership and stewardship, classification and purpose limits, access approvals tied to least privilege, lineage and metadata expectations, and retention and disposal rules that apply to training and analytical artifacts. Real-world scenarios include analytics environments becoming data dumping grounds, teams training models on data without documented consent or provenance, and leaders making decisions from dashboards that lack reliable definitions and quality controls. For CGEIT scenarios, the best answers usually strengthen governance by embedding data controls into analytics workflows, requiring traceable evidence, and balancing innovation with enforceable standards that keep risk visible and manageable. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.