How Enterprise RAG Systems Handle Large Data and Strict Access Control

Building a production-grade Retrieval-Augmented Generation (RAG) system for enterprise use requires solving two core challenges: managing large datasets efficiently and enforcing strict data access controls. Unlike basic RAG prototypes, enterprise deployments must break documents into smaller, metadata-tagged chunks to avoid overwhelming language models and reduce cost and latency. Security is enforced through Attribute-Based Access Control (ABAC), where every stored vector is tagged with ownership attributes so users can only retrieve data they are authorized to see. The proposed architecture combines FastAPI, PostgreSQL, Qdrant, and LangChain, keeping relational data and vector storage strictly separated for clarity and security. Access restrictions are applied deterministically at the database level, ensuring the underlying language model never processes data beyond a user's clearance.