LLM perimeter defenses: how an AI gateway reduces leakage and prompt injection risk

A technical guide explains how to build an AI gateway around an LLM as a unified entry point for all model calls, using multiple layers of checks to reduce the risk of data leakage and prompt injection. The gateway is split into seven stages: inbound does desensitization and injection detection and selects a model by data category; outbound checks for leakage and uses a tool whitelist to constrain the actions an agent can execute. The article cites AgentDojo tests on GPT-4o: the tool whitelist let about 7% of attacks through, and the injection detector had a similar rate, yet it lowered the task completion rate under no attack from 69% to 41%. For deployment, rules and small classifiers are placed on CPU and large guard models on GPU, with latency and rental price references given; it also compares Russian and Western products and outlines regulatory requirements such as FSTEC Order No. 117.