Why AI Governance Belongs in Your Gateway, Not Your Policy Docs

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Most teams enforce AI governance at the application level, leading to inconsistent PII redaction, fragmented logs, and untracked provider keys across services. This fragmented approach fails to provide a unified, auditable record of every model call — a gap that widens as organizations deploy more LLM-powered features and autonomous agents. An AI gateway addresses this by sitting between all applications and model providers, enforcing policies and logging every request in one place regardless of which team or SDK originated it. Developers can redirect existing OpenAI-compatible clients to a gateway with minimal code changes, while routing, credentials, and compliance rules are managed centrally. Centralizing governance as infrastructure — rather than per-app code — makes it possible to answer cross-cutting audit questions and enforce consistent controls at scale.

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