Secure Enterprise RAG Implementation

Document-level permissions must be enforced at retrieval time—users should only retrieve documents they're authorized to see.

Implementation approaches:

Critical consideration: Access control must happen in the retrieval layer, not just the UI. A user shouldn't be able to craft queries that surface unauthorized content.

Options for handling personally identifiable information:

Redaction before embedding — Replace PII with tokens, store mapping separately. Pros: PII never enters vector DB. Cons: Loses semantic information.

Separate indexes — PII documents in restricted namespace with stricter access. Pros: Granular control. Cons: More complex architecture.

Query-time filtering — Detect PII in results and mask before returning. Pros: Preserves search capability. Cons: Risk of leakage in edge cases.

On-premise processing — Use local embedding models to avoid sending PII to external APIs. Pros: Complete control. Cons: Higher infrastructure cost.

Define clear data classification policies before implementation.

VPC Deployment — Vector DB and LLM inference within your private network. Good balance of security and capability.

Self-Hosted Models — Llama, Mistral, or other open models for complete data control. No data leaves your infrastructure. Consider fine-tuning for domain performance.

Regional Compliance — Deploy in specific regions for data residency (GDPR, data sovereignty). Major cloud providers offer regional deployment options.

Air-Gapped Deployment — Fully offline for highest security requirements. Requires local models and on-premise vector databases. Most restrictive but necessary for certain industries.

Balance security requirements with capability needs—more restricted often means less capable models or higher infrastructure costs.