AI Database Proxy — One Gateway for AI Agents and Your Databases

What is an AI database connection proxy?

An AI database proxy is a service that brokers every AI-agent database call — replacing the pattern where each agent holds its own database credentials. It accepts queries (SQL, Mongo, Redis commands, Elasticsearch DSL, ChromaDB), evaluates them against policy, executes them on the real database, and returns processed results to the agent.

The database proxy is where you put the controls that the database itself can't express:

• Per-agent identity instead of shared DB credentials, so revocation and audit attribution work on a per-agent basis.
• Query guardrails that classify a statement as low / medium / high risk before execution, not after.
• Field-level masking on the agent result path — sensitive values are replaced with stable secret-backed pseudonymous hashes before the row is returned to MCP / Skill / CLI callers.
• Local hash-chained audit logs (with a built-in audit verify CLI) that answer "which agent ran what, when, and why was it allowed." Local verification only — see trust and limitations for scope.

Why AI agents need a proxy

Handing an LLM raw database credentials is fast to set up — and easy to regret. These risks show up in production:

1. Destructive queries. Agents hallucinate. A "clean up stale rows" task becomes DELETE FROM users with nobody awake to catch it.
2. Credential leakage. Connection strings end up in prompts, logs, and trace exports — eventually in someone else's training data.
3. PII shipped to third-party models. Customer names, card numbers, and biometric data get serialized into prompts and sent to whichever LLM the agent runs on.
4. No audit trail. "An agent did it" isn't an answer your incident review or compliance reviewer will accept. You need who, what, when, and why-was-it-allowed — written somewhere you can verify later.
5. Every agent has too much privilege. Agents inherit developer credentials — usually read/write on every table. Least-privilege doesn't apply when a chatbot can TRUNCATE or DELETE.
6. Or every agent has too little privilege. Lock agents down too tightly and they can't actually finish the work — automation stalls and humans have to babysit every step.

How FutrixData implements the proxy

Every database operation routes through a single execution manager. Static rules and database-specific probes evaluate it before the database adapter ever runs.

The full flow:

1. Agent sends a query over MCP, Skill, or CLI.
2. FutrixData resolves data source, parses the statement, and matches static rules.
3. For SQL, MongoDB, and D1, a pre-execution EXPLAIN probe inspects scan rows, index usage, and joins. For Elasticsearch, ChromaDB, DynamoDB, and Redis, a static risk engine performs command-level checks instead.
4. The combined risk level (allow / warn / require_approval / block) is reconciled with the per-source trust mode (Approval / Cautious / Trusted / Danger).
5. The database adapter executes, results are masked field-by-field, and an audit entry is written.

CLI, MCP, Skill, and the in-app AI Chat all share the same service-layer policy — they cannot diverge.

What FutrixData enforces at the proxy layer

Supported databases

FutrixData proxies MySQL, PostgreSQL, MongoDB, Redis, Elasticsearch, ChromaDB, DynamoDB, and Cloudflare D1 through one interface. Each database type has its own parser, rule set, and probe — Redis is not treated as SQL with strings replaced.