How to Build a Secure AI Gateway for LLM Applications

Build an AI security gateway that controls access to LLMs, RAG systems, and tools.

Architecture

User -> API Gateway -> Auth -> Rate Limit -> Policy -> LLM
                                             |-> RAG
                                             |-> Tools
                              Output Controls -> Monitoring

Controls

Authentication establishes identity. Rate limiting controls abuse and cost. Input controls reject malformed or oversized requests.

RAG Authorization

User -> Authorization -> Search -> Document ACL -> Context -> LLM

Never assume every indexed document is available to every user.

Tool Gateway

LLM -> Tool Gateway -> Authorization -> Risk Policy -> API

Validate resource, operation, destination, schema, and business rules.

Secrets

The model should request an action; the application/tool gateway should retrieve credentials from a secret manager after authorization.

Monitoring

Track user, tenant, model, tokens, requests, tool calls, security decisions, and cost while avoiding unnecessary sensitive prompt logging.

Final Takeaway

An AI gateway keeps authentication, authorization, data access, tool execution, secrets, and monitoring outside the model's control.