AI Gateway Security: How to Protect LLM Traffic in Production
Blog post from NeuralTrust
AI gateways are presented as inline security controls for production LLM and agent traffic, inspecting prompts, tool calls, and model responses semantically rather than relying on the syntax-based protections of traditional WAFs and API gateways. Because agents can access internal systems and execute actions, prompt injections embedded in user input, retrieved documents, or tool outputs can lead to unauthorized data access or operational effects, making centralized controls especially important. Key gateway capabilities include prompt-injection detection, inbound and outbound PII redaction, scoped token and tool permissions, rate limits and quotas, fail-closed policies, data-residency routing, and tamper-resistant audit logging. These controls address several OWASP LLM Top 10 risks, particularly prompt injection, sensitive-information disclosure, excessive agency, system-prompt leakage, and unbounded consumption, while supply-chain and training-data poisoning require additional upstream measures. The discussion also links gateway logging and access controls to IBM-reported AI incident trends and EU AI Act requirements for high-risk systems, and distinguishes TrustGate’s real-time policy enforcement from TrustGuard’s broader risk assessment, adversarial testing, and governance functions.
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