How to Build a Secure Enterprise Chatbot on Your Own Data

The architecture in one picture

Documents → chunking + metadata (owner, sensitivity, source) → embeddings → vector DB. At query time: user question → permission filter → retrieve top-k chunks → LLM answers with citations → output validated → logged. The model never sees documents the user cannot access.

Security checklist we enforce

• Zero-data-retention API tiers or private model deployment (VPC / on-prem / dedicated cloud)
• PII redaction before embedding and before model calls
• SSO + role-based access; retrieval filtered by user permissions
• Encryption in transit (TLS) and at rest (KMS-managed keys)
• Prompt-injection defenses and output validation (schema checks, toxicity)
• Full audit log: who asked what, which sources were used, what was answered
• DPA with the model provider; no training on your data

Evaluation before launch

Build a golden set of 100+ real questions with expected answers and source documents. Measure retrieval accuracy (did we fetch the right chunks?) and answer groundedness (is every claim cited?). Gate every release on these scores — this is what separates a demo from production.

Deployment options

Public cloud with zero-retention APIs is fastest for non-sensitive data. For HR, finance, legal or healthcare data, we deploy models in your VPC or on-prem (Llama, Qwen, Mistral via vLLM/Ollama) so data never leaves your perimeter. Hybrid is common: private models for sensitive corpora, frontier APIs for general tasks.

CredenceMind builds and operates these systems for clients — from pilot in 2–3 weeks to production in 8–12.