AI & data analytics governance best practices

This post details how to build governance that works for analytics and AI without blocking the work that matters. It covers LLM and generative AI governance, the business case for investment, regulatory frameworks (EU AI Act, NIST AI RMF, ISO 42001), operating models (centralized, federated, hybrid), and best practices for policies and controls including quality standards for AI inputs and access management. It emphasizes the need for clear accountability, transparency, systematic bias detection, protection of data and models, complete documentation trails, and meaningful human oversight over automated decisions. The post also highlights the risks of shadow AI and the importance of treating AI-generated content as a distinct data category with its own quality checks, lineage tracking, and approval workflows.