Schema Confidence Gap: AI Data Quality Risks Explained

This blog post from Liquibase outlines a "schema confidence gap" where organizations run AI/LLMs against production databases without mature schema governance, describes risks such as model degradation, audit failures, and uncontrolled change, and promotes Liquibase Secure as a platform to enforce automated policy checks, produce audit-ready evidence, detect drift, and provide cross-platform governance.