10 AI Governance Best Practices Enterprise Teams Need in 2026
DATAVERSITY, Thursday, October 15th, 2026
Ten practices for enterprise AI governance, from AI asset inventories and risk tiers to dynamic accountability chains.
The article argues that AI governance has evolved from reactive use policies into comprehensive frameworks that extend data governance practices such as lineage, quality, access control and ownership.
Its practices include a continuously updated AI asset inventory that covers embedded SaaS AI, with automated discovery to surface shadow AI.
It recommends tiering use cases by risk factors such as data sensitivity, autonomy and regulatory exposure, with many enterprises adopting a five-tier model aligned with the EU AI Act.
It also calls for dynamic chains of responsibility for approving, pausing and retiring AI systems.