AI and Data Analytics Governance: How to Control Access Without Blocking AI or Redacting Your Data

AI and analytics governance addresses how AI agents and users access, query, and act on warehouse data as agent activity increases in volume, speed, and potential exposure compared with traditional human access patterns. The piece argues that outright AI bans can drive unmonitored use and that broad pre-redaction can both miss sensitive information and reduce data utility, advocating instead for query-time, zero-trust enforcement based on each requester’s identity. It recommends carrying user identity through AI-generated warehouse queries so existing row-level and column-level security policies apply without relying on broad service accounts or maintaining separate AI permissions. It also emphasizes comprehensive audit records for AI actions, regular review and deprovisioning of agent identities, and monitoring model usage to control both compliance risks and spending. Sigma is presented as a warehouse-native platform that inherits security policies from systems such as Snowflake, Databricks, BigQuery, and Redshift, while providing governed data models, logging, and AI usage reporting.

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