Retrieval Augmented Generation for Enterprise Security Intelligence in Cloud Based Decision Support and Knowledge Systems

Abstract

Retrieval-Augmented Generation (RAG) has emerged as an important architecture for improving the reliability, relevance, and contextual grounding of generative artificial intelligence in enterprise environments. Cloud-based organizations generate large volumes of heterogeneous security information through security information and event management platforms, vulnerability databases, incident reports, access-control systems, threat-intelligence feeds, audit logs, security policies, and technical documentation. Conventional generative AI systems may struggle to provide trustworthy security intelligence when their responses depend primarily on static training knowledge or incomplete organizational context. RAG addresses this limitation by connecting a language model with external knowledge repositories and retrieving relevant evidence before generating a response. This enables enterprise security applications to incorporate current organizational information while reducing dependence on information encoded during model training. The proposed research investigates RAG as a framework for cloud-based security intelligence and decision support, focusing on knowledge retrieval, contextual reasoning, evidence-grounded generation, security-event interpretation, and decision assistance. The methodology combines enterprise security datasets, document and event preprocessing, vector-based retrieval, hybrid search, language-model generation, and quantitative evaluation. System performance is assessed using retrieval precision, recall, response relevance, factual grounding, hallucination rate, response latency, and decision-support usefulness. The study aims to establish a systematic approach for integrating RAG into enterprise security knowledge systems while addressing data freshness, access control, privacy, explainability, and reliability