dbt column-level lineage maps each dbt model column back to the source columns, expressions, filters, joins, and transformations that produced it. Learn why compiled SQL matters and where catalogs need semantic lineage.
Table-level permissions are not enough for Text-to-SQL. This guide explains how field-level permission checks detect sensitive columns and enforce policy before generated SQL reaches the database.
A practical framework for evaluating whether LLM-generated SQL is ready for production governance, covering parsing, catalog binding, sensitive fields, policies, lineage, and audit readiness.
A practical reference architecture for securing LLM-generated SQL before execution, covering parser, catalog binding, policy engine, risk scoring, repair loops, and audit logs.
Learn why LLM-generated SQL needs semantic validation: catalog binding, name resolution, type checks, joins, permissions, and repair feedback.
Prompt rules can improve LLM-generated SQL, but they cannot prove a query is safe, authorized, semantically valid, or auditable. Production Text-to-SQL needs deterministic SQL validation before execution.
Before a Text-to-SQL system reaches production, teams should validate more than SQL syntax. This checklist covers 10 risks: unsafe statements, hallucinated fields, PII exposure, permission bypass, high-cost queries, wrong joins, audit gaps, and more.
Enterprises should not let LLMs execute SQL directly because generated queries need deterministic validation, permission checks, risk scoring, and audit before reaching a database.
An LLM SQL Guard checks AI-generated SQL before execution and returns structured feedback that helps an LLM produce safer, more accurate queries.
A deep technical guide to SQL parser ASTs, bound ASTs, Logical IR, Semantic IR, logical plans, and relational algebra for parser, lineage, and governance engineers.
