Technical cluster
LLM gateway architecture for enterprise AI security.
A secure LLM gateway gives enterprises a control point for model provider traffic, prompt inspection, sensitive data masking, and audit evidence before requests reach external AI systems.
Traffic
Gateway aligned
Apply controls before prompts reach external model providers.
Control
Policy first
Map every AI interaction to allow, flag, mask, or block decisions.
Evidence
Audit ready
Keep explainable records for security, risk, and compliance reviews.
The core layers of an enterprise LLM gateway
A practical architecture includes identity and application context, prompt inspection, AI DLP, policy evaluation, provider routing, response controls, and audit logging. Each layer should produce evidence that can be reviewed by security and governance teams.
Architecture
AI proxy layer
The enforcement point that normalizes provider traffic and applies policy.
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RAG
Secure RAG pipeline
Protect retrieval context, prompt composition, and downstream outputs.
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Routing
Multi-provider routing
Keep policy consistent while supporting multiple model providers.
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Why API gateways alone are not enough
General API gateways are excellent for authentication, rate limiting, and network routing. They usually do not understand prompt content, sensitive entity context, prompt injection patterns, or AI-specific audit decisions. That is why buyers pair gateway architecture with prompt firewall and AI DLP controls.
Design your LLM gateway control layer
Review provider routes, sensitive data paths, and policy requirements with PromptWall.
Frequently asked questions
What is LLM gateway architecture?+
It is the design of the control layer between applications or users and model providers, including routing, prompt inspection, AI DLP, policy, and audit.
Does an LLM gateway replace application security?+
No. It complements application security by adding AI-specific content and provider controls that traditional AppSec usually does not cover.