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Data Loss Prevention for LLM Traffic: Where It Has to Sit

Por Ashish Dubey

Published: September 28, 2026

Data Loss Prevention for LLM Traffic — TrueFoundry

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Preguntas frecuentes

What is data loss prevention for LLM traffic?

Inspecting and controlling sensitive data before it leaves your perimeter in a prompt, tool call or model response. It differs from classic DLP because there is no file and no unsanctioned destination - the payload is JSON inside a TLS session to an API you pay for. Controls must understand the request schema and sit at an authenticated egress chokepoint.

Why does classic network DLP miss LLM traffic?

Three reasons compound. The destination is allowlisted by design, so destination rules never fire. The content sits in a nested conversation array rather than a file or form field, which document-oriented engines parse poorly. And without TLS inspection there is nothing to parse - while even an inspecting proxy cannot tell which caller sent the request.

Can an AI gateway block requests containing PII?

It depends on the detector. TrueFoundry’s built-in PII / PHI Detection is mutate-only - it always redacts, never rejects. For a hard block you need a validate-capable integration such as AWS Bedrock Guardrails, Azure PII or CrowdStrike. “PII detection” in a feature list never tells you whether the outcome is a redaction or a 400.

Can I deploy TrueFoundry in my own VPC or on-prem?

Yes. TrueFoundry runs in your VPC, on-prem, air-gapped, or hybrid, so prompts and responses never leave your domain even as you route across many providers.

Does TrueFoundry support MCP and AI agents generally?

Yes. It includes an MCP Gateway, an Agent Gateway, and an MCP & Agents Registry with tool-level access control. Agents on LangGraph, CrewAI, AutoGen, or a custom framework can all be governed centrally.

¿Se integra con mi pila de observabilidad existente?

Sí. La pasarela es compatible con OpenTelemetry y se integra con Grafana, Datadog, Prometheus o tu pila preferida. Rastrea cada solicitud desde la instrucción (prompt) hasta la ejecución de la herramienta y el modelo, así obtienes un registro unificado sin tener que reemplazar lo que ya tienes en funcionamiento.

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