Enterprise AI Architecture: Secure Private RAG Pipelines

As enterprises adopt Large Language Models, the primary blocker is preventing Data Leakage and guaranteeing Hallucination-Free Factuality.

By combining Retrieval-Augmented Generation (RAG) with strict enterprise tenant isolation and Gemini API capabilities, organizations can query millions of internal PDF manuals, contracts, and architecture design docs with zero data leakage.

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Architecture Flow Diagram

[Enterprise User] ---> [OIDC Authenticated Frontend]

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                   [API Gateway + RBAC Filter]

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              [Embedding Model (Gemini Embedding)]

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                    [Vector Search Database]

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                  [Retrieved Context Chunks]

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                   [Gemini 3.6 Flash LLM]

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                   [Verified Grounded Answer]

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Security & Compliance Architecture Rules

1. Zero Data Retention in Model Training: Ensure API calls use enterprise endpoints where prompt data is NOT saved or used for base model training.
2. Metadata Access Control: Store document ACLs (Access Control Lists) directly alongside vector embeddings so retrieval strictly filters chunks the requesting user is allowed to read.
3. Audit Trail Logging: Every query, retrieved chunk ID, and generated completion is saved to immutable Cloud Logging buckets for SOC2 compliance.