96.5% of orgs run AI on production databases. Learn why database change governance matters more than model risk, and how leading teams govern AI-speed change.

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96.5% of organizations now have AI or LLMs touching their production databases, yet most database change governance still relies on informal checklists and inconsistent controls. Real-world incidents show AI agents causing outages and data loss when allowed to act on live systems without enforced guardrails. The core risks aren't model hallucinations but data quality issues, ungoverned AI-generated SQL, and schema drift. Leading teams are addressing this by treating governance as the default operating mode, using machine-readable change definitions (XML/YAML), shifting controls left before CI, and building automatic audit trails. Key practices include change-as-code, policy-as-code, and tracking metrics like MTTD, MTTR, and AI Governance Coverage to make database readiness measurable rather than assumed.

AI is Already in Your Database: The Real Risk is How You Govern Change | Liquibase

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The governance gap: when “sometimes” is not a control

What leading teams are already doing differently

What “Database Change Governance” really means

AI is already in your data. The next move is yours.