The notion that Text-to-SQL is a solved problem is a dangerous myth, as LLMs can generate non-deterministic SQL queries that pose risks to sensitive data. Approaches like feeding the entire schema to the LLM or using semantic proxy layers can lead to issues such as data corruption or context window limitations. A more robust solution involves a "Hard-Gated SQL Sandbox" that uses an Abstract Syntax Tree (AST) validator to check generated SQL for unauthorized access or joins before execution, alongside resource governance at the database level to prevent excessive compute costs. AI
IMPACT Highlights critical security and cost management considerations for deploying LLM-powered data access tools in production environments.
RANK_REASON The article discusses potential risks and architectural patterns for using LLMs in Text-to-SQL scenarios, offering an opinionated perspective rather than announcing a new product or research finding.
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