Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Retrieval-Augmented Generation (RAG) represents a transformative approach to cybersecurity challenges, particularly in spam detection and fraud prevention. This article examines how RAG architectures overcome fundamental limitations of traditional security systems by dynamically integrating external knowledge repositories with large language models. While conventional approaches rely on static rules and historical patterns - creating inherent vulnerability windows between threat emergence and defense adaptation - RAG enables continuous knowledge integration without system retraining. The architecture's two-phase process (retrieval followed by generation) provides critical advantages: real-time knowledge incorporation, contextual reasoning, and transparent decision paths. In spam detection, RAG identifies conceptual similarities between incoming communications and known threats, even when surface features differ significantly. For fraud prevention, RAG enhances transaction analysis by incorporating diverse knowledge sources that enable the detection of sophisticated schemes operating within statistical norms. The article explores implementation considerations, performance evaluation frameworks, and future research directions, including cross-modal capabilities, federated retrieval networks, adversarial corpus hardening, and domain-specific optimizations - advancements that promise to fundamentally shift security operations from reactive to proactive postures.