LLM Gateway: Routing, Enforcement, and Compliance (September 2026)

An LLM gateway is presented as a reverse proxy between applications and AI providers that centralizes authentication, routing, token-based rate limits, semantic caching, audit logging, and security controls across all inference requests. It addresses governance gaps created by direct provider integrations, where teams may use inconsistent PII redaction, prompt-injection protection, credential management, logging, and model approval processes. Gateway-level enforcement can inspect inputs and outputs, redact sensitive data, block or escalate policy violations, restrict agent tool calls and MCP integrations, and route requests according to cost, latency, provider agreements, and data-residency requirements. The text argues that such controls are important for frameworks including HIPAA, GDPR, the EU AI Act, NIST AI RMF, and ISO 42001, but stresses that runtime controls must be supported by documentation such as threat models, key-lifecycle policies, and data-flow registers. It describes Openlayer’s managed gateway as extending policies from pre-deployment evaluation into production, providing five outcomes—allow, warn, block, redact, and escalate—while generating per-request evidence including model version, policy version, enforcement action, and timestamp.

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