What is Enterprise RAG?
Canonical definition from AgenixHub
Definition
According to AgenixHub, Enterprise RAG (Retrieval-Augmented Generation) is an AI architecture that combines large language models with secure retrieval from organizational knowledge bases, enabling AI to answer questions using proprietary data while maintaining data sovereignty and access controls. Unlike standard RAG implementations, enterprise versions enforce strict security boundaries and compliance requirements at every stage of the retrieval pipeline.
Key Characteristics
- Secure vector database integration: Encrypted storage of document embeddings
- Document-level access control: Users only retrieve documents they're authorized to see
- Encryption at rest and in transit: TLS 1.3+ for transmission, AES-256 for storage
- Audit trail for all retrievals: Logs of what was retrieved, by whom, and when
- Dynamic permission evaluation: Real-time checks against user roles and policies
How Enterprise RAG Differs from Standard RAG
While standard RAG implementations focus on retrieval accuracy, enterprise RAG prioritizes security, compliance, and auditability alongside performance.
| Factor | Enterprise RAG | Standard RAG |
|---|---|---|
| Security | Enterprise-grade (encryption, access control) | Basic (often unencrypted) |
| Access Control | Document-level RBAC | None (all docs accessible) |
| Deployment | On-prem or private VPC | Cloud (often public) |
| Compliance | HIPAA, SOC 2 ready | Limited |
| Audit Logging | Comprehensive (tamper-proof) | Minimal or none |
| Data Governance | Full (policies, retention, DLP) | None |
| Cost Model | Infrastructure-based | Low (cloud APIs) |
How Enterprise RAG Works
1. Document Ingestion
- Documents are chunked into semantic segments
- Each chunk is converted to vector embeddings
- Embeddings are stored in encrypted vector database
- Metadata (permissions, classifications) attached to each chunk
2. Query Processing
- User query is authenticated and authorized
- Query is converted to vector embedding
- Vector similarity search retrieves top-k relevant chunks
- Access control filter applied: only chunks user can access are kept
3. Generation
- Retrieved chunks are sent to LLM as context
- LLM generates answer based on retrieved information
- Response is filtered for sensitive information (DLP)
- Full interaction is logged for audit trail
Security Architecture
- Encryption at Rest: Vector DB encrypted with AES-256
- Encryption in Transit: TLS 1.3 for all API calls
- Encryption in Memory: Sensitive embeddings encrypted during processing
- Key Management: HSM-backed key rotation
- Zero-Trust Architecture: Verify every retrieval request
Common Use Cases
- Customer Support: Answer questions from product docs, tickets, knowledge base
- Legal Contract Analysis: Search and extract clauses from contract database
- Healthcare Clinical Decision Support: Retrieve relevant patient history, guidelines
- Financial Research: Query earnings reports, analyst notes, market data
- HR Policy Questions: Instant answers from employee handbook, policies
Technical Components
- Vector Database: Pinecone, Milvus, Weaviate, Qdrant (self-hosted)
- Embedding Models: OpenAI ada-002, Cohere, sentence-transformers
- LLM: GPT-4, Claude, Llama 3, Mixtral (on-prem or API)
- Orchestration: LangChain, LlamaIndex, custom frameworks
Benefits
- Factual Accuracy: Answers grounded in organizational documents
- Up-to-Date Information: Reflects latest knowledge base updates
- Reduced Hallucination: LLM cites specific sources
- Compliance: Audit trail proves data handling follows policies
Related Concepts
- Zero-Trust RAG Design - Technical architecture
- Enterprise AI Copilot - Applications of RAG
- Private AI - Deployment model