SHIELD-RAG Generative AI for Proactive Threat Detection in Financial Systems

Financial systems today are more susceptible to advanced cyber attacks, which call for the use of advanced and proactive threat detection systems beyond conventional rule-based or anomaly-driven frameworks. Current methods are prone to problems like high false positive rates, shallow contextual understanding, and sluggish reaction times, which can lead to financial losses, reputations, and non-compliance with regulatory requirements. While some work has been conducted on the application of machine learning and natural language processing for security analysis, these efforts are mostly in silos and lack a coherent framework that dynamically responds to new threat intelligence. To address this gap in research, this work presents SHIELDRAG (Security-Hardened, Integrated, Explainable, and Learning-Driven Retrieval-Augmented Generation), a new generative AI system specifically tailored for proactive threat detection in financial environments. As the opposite of conventional detection systems, SHIELD-RAG combines the strengths of Retrieval-Augmented Generation (RAG) with domain-aware embeddings and an ever-evolving security knowledge base, enabling real-time threat prediction, contextual alerting, and remediation insights. Through retrieval of pertinent threat intelligence information and generation of explainable narratives on suspicious patterns, the model enhances situational awareness and provides analysts with greater accuracy and timeliness in decision-making abilities. The proposed methodology not only addresses the critical challenge of explainability in AI-based threat detection but also bridges the gap between static monitoring as well as adaptive learning. Initial simulations and dataset testing demonstrate dramatic improvement in detection accuracy, interpretability, and response times over current benchmarks. This work positions SHIELD-RAG as a paradigm-shifting financial cybersecurity innovation with implications for broader applications to regulated industries requiring real-time, reliable, and intelligent threat reaction systems.