Retrieval-Augmented Generative AI Frameworks for Secure Enterprise Knowledge Management and Data Intelligence

Abstract

Retrieval-Augmented Generative Artificial Intelligence (RAG) frameworks are emerging as an effective approach for improving enterprise knowledge management and data intelligence by combining information retrieval with large language models. Traditional enterprise knowledge systems often struggle with fragmented data sources, outdated information, limited contextual understanding, and security constraints. This study proposes a secure RAG-based framework that integrates enterprise data repositories, semantic retrieval, document processing, vector databases, access-control mechanisms, and generative AI to provide context-aware and trustworthy knowledge services. The framework enables organizations to retrieve relevant information from structured and unstructured data sources before generating responses, thereby reducing dependence on static model knowledge and improving answer relevance. Security mechanisms, including identity-based access control, encryption, data classification, audit logging, and retrieval filtering, are incorporated to protect sensitive enterprise information. The proposed methodology further introduces data preprocessing, embedding generation, hybrid retrieval, reranking, contextual prompt construction, response generation, and continuous evaluation. Performance can be assessed using retrieval precision, response relevance, factual consistency, latency, security compliance, and user satisfaction. The framework supports intelligent enterprise search, decision support, document analysis, regulatory knowledge management, and organizational intelligence while maintaining governance and privacy requirements. The study demonstrates how secure RAG architectures can provide a scalable foundation for enterprise knowledge management and data-driven decision-making.