A query firewall for AI is a runtime enforcement point between an agent and a database. It parses each query the model produces, evaluates it against a policy, and either executes it or rejects it — before anything reaches your data.
How it differs from the firewalls you already have
Network firewalls reason about hosts and ports. They cannot tell a legitimate SELECT from one that exfiltrates your customer table — both are the same TCP connection.
Prompt firewalls and LLM guardrails inspect text going into or out of the model. Useful, but probabilistic: they try to catch a bad instruction rather than the bad query it produced.
A query firewall inspects the artifact that actually touches your database. Enforcement is deterministic, because SQL structure is something you can parse and verify.
What it must enforce
Statement-type allow-listing at the AST level — SELECT only, no stacked statements, no DDL;
table, column, and function allow-listing, denying by default;
a mandatory LIMIT and query timeout, so a single call cannot drain or stall the database;
per-agent row-level security, evaluated outside the model;
an append-only audit log of every allow and deny decision.
QueryShield implements each of these as a proxy in front of your database. Because the agent calls the proxy instead of the database, enforcement cannot be bypassed — there is no direct path around it.
Enforce this automatically with QueryShield
A secure SQL proxy for AI agents: natural language in, SELECT-only validated SQL out, per-agent row-level security, and an append-only audit log. Your agents never see connection strings.