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.
