Post by AIxBlock, Inc

Your security team spent three months hardening the inference layer. Prompt logging locked inside the VPC. RAG boundaries enforced. Output classification catching sensitive echoes before they reach the user. Then someone asks where the training data was labeled. The room goes quiet. That is the gap in private self-hosted LLM platforms data leakage prevention almost nobody funds. Teams solve inference-time leakage in obsessive detail, then give the training pipeline one line in the security review: "labeling vendor, DPA on file." A DPA is a contractual promise, not architecture. The vendor still holds your data. No runtime guardrail reverses a leak that already happened eight months ago. Leakage lives at two layers, and most procurement only covers one: 🔍 Inference-time — plaintext prompt logs, RAG crossing tenant lines, prompt injection through retrieved documents. vLLM, Bedrock with PrivateLink, and Azure OpenAI Private Link are built for this and mostly handle it well. 📁 Training-time — raw call recordings, transcripts, and RLHF preference rankings uploaded into a SaaS annotation tool months before the model ran. The content crossed the perimeter during labeling. The inference server did not exist yet. The detail teams miss most: RLHF preference data does not just expose content. It exposes what your model is being taught to refuse, escalate, and default to. That sits entirely upstream of any inference control. The fix is a self-hosted annotation environment. Labeling tooling inside your perimeter, source content flowing client storage to client storage, annotators on scoped accounts to data they can see but cannot exfiltrate. That is what self-hosted means at the data layer. Not the same as self-hosting an LLM. Different problem, different layer, different vendor. In our latest newsletter, we map the three layers a defensible architecture needs, inference, data, and audit, and why most procurement only funds the first. Read the full newsletter below ↓ #EnterpriseAI #AIxBlock #LLM #DataGovernance #DataLeakage #RegulatedAI #SpeechAI #TrainingData #DataSovereignty #MLOps