Proof/Enterprise Knowledge & Security

Zero-Trust Private RAG over 10M+ Enterprise Documents

Implementing document-level RBAC/ABAC authorization filtering, hybrid sparse-dense vector search, and a self-corrective hallucination grader across 10 million internal documents.

September 202611 min read

Zero-TrustRBAC / ABACHybrid SearchSelf-Corrective RAG

Technical Architecture

System Architecture · Zero-Trust Private Enterprise RAG

FIGURE 9.0 — ZERO-TRUST RBAC RETRIEVAL & GRADER TOPOLOGY100% On-Prem / VPC Deployable

Summarize with:

Enterprise knowledge bases fail in production when they ignore security permissions. If an intern's search query can retrieve an unredacted executive compensation spreadsheet or unreleased M&A document through vector similarity, the AI represents a catastrophic data leak.

We built a zero-trust enterprise retrieval engine indexing 10M+ documents across SharePoint, Google Workspace, Confluence, and Jira, enforcing row- and document-level Active Directory authorization before any chunk reaches the generative model.

Pre-retrieval security trimming and token-level authorization

Filtering documents after retrieval is flawed because top-K similarity search will waste vector slots on classified documents the user cannot view, degrading answer relevance.

We implemented cryptographically verified pre-retrieval security filters directly inside the vector index. Search queries only execute against vector partitions matching the user's Okta/Entra ID security groups.

“Security trimming belongs inside the retrieval index, not as an afterthought filter.”

Self-corrective RAG and hallucination grading

Retrieved chunks pass through an automated relevance grader. If the retrieved context is insufficient or conflicting, the system rejects the answer and triggers query rewriting rather than hallucinating plausible false facts.

Enterprise retrieval benchmarks

Dimension	Metric
Corpus scale	10M+ documents (4.8B tokens)
P95 Query latency	850ms including security resolution
Authorization breach rate	0.00% (Mathematical RBAC/ABAC guarantees)
Answer fidelity	97.4% verifiable citations
Search index	Hybrid BM25 sparse + dense vector Reciprocal Rank Fusion

Executive Engineering Takeaway

Engineering Principle in Production

Implementing document-level RBAC/ABAC authorization filtering, hybrid sparse-dense vector search, and a self-corrective hallucination grader across 10 million internal documents.

Breakdowns

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