How an AI Gateway Solves AI Governance for Enterprise

Enterprise AI governance requires technical enforcement rather than policies alone, and the text presents an AI gateway as the central infrastructure layer for applying controls across all interactions between applications and language-model providers. It argues that gateways can inspect prompts and completions in real time, enforce content, data-handling, routing, and prompt-injection policies, provide identity-aware access controls, and create structured audit logs for observability and compliance reporting. These capabilities are positioned as supporting obligations under frameworks including the EU AI Act, GDPR, and the UK NCSC AI guidance, particularly through PII redaction, jurisdictional routing, monitoring, and evidence generation. The proposed implementation approach involves routing all AI traffic through a central gateway, cataloguing applications and their risk profiles, configuring policies and limits, integrating logs with SIEM tools, and using periodic audits to identify gaps. The text promotes NeuralTrust’s TrustGate as a self-hosted gateway that combines these functions without application-level changes, while distinguishing AI gateways from traditional API gateways through their ability to understand and govern LLM content rather than only network requests.

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