How to safely let LLMs query your databases via sandboxed materialized views (www.pylar.ai)

A new practical guide has been released focusing on building secure AI agents capable of querying databases without compromising data security, compliance, or operational efficiency. The guide outlines a five-layer architecture designed to mitigate risks associated with granting direct database access to AI agents, including the exposure of sensitive data, compliance challenges, and operational issues arising from ad-hoc querying. Instead of allowing direct database credentials, the architecture emphasizes controlled interactions through materialized views, ensuring that agents only access authorized data. This layered approach includes defining what data agents can access using materialized views that incorporate security filters, aggregations, and pre-computation strategies. Key features of the architecture include row-level and column-level security, data masking, and the use of Model Context Protocol (MCP) tools to create secure, well-documented APIs for AI agents. This structured method not only enhances security and compliance—crucial for adhering to regulations such as GDPR and HIPAA—but also improves performance and stability, allowing organizations to confidently leverage AI capabilities while managing data governance effectively.