LLM-Enhanced Adaptive Machine Learning Framework for Financial Cloud Security Cache-Aware Threat Detection, Multi-Modal Risk Analytics, Flash Storage Optimization, and ERP Integration
DOI:
https://doi.org/10.15662/IJRAI.2022.0504005Keywords:
Financial cloud security, Adaptive machine learning, Large language models, Cache-aware threat detection, Multi-modal risk analytics, Flash storage optimization, ERP integration, Cyber threat detection, Predictive analytics, Real-time anomaly detection, Enterprise security, AI-driven cybersecurity, Cloud-based risk management, Operational resilience, Threat intelligenceAbstract
The increasing sophistication of cyber threats in financial cloud infrastructures demands intelligent, adaptive, and high-performance security frameworks. This paper presents an LLM-enhanced adaptive machine learning framework designed to strengthen financial cloud security through cache-aware threat detection, multi-modal risk analytics, and flash storage optimization, while seamlessly integrating with ERP systems. The framework leverages large language models (LLMs) to enhance anomaly detection, predictive risk assessment, and real-time threat intelligence across transactional and operational data streams. Cache-aware mechanisms improve data access efficiency and reduce latency in threat detection pipelines, whereas flash storage optimizations support high-speed data processing for large-scale financial operations. Multi-modal analytics combines behavioral, transactional, and network data to classify and prioritize threats accurately. ERP integration ensures enterprise-wide data consistency, operational visibility, and automated response capabilities. Experimental evaluations demonstrate improved detection accuracy, reduced false positives, optimized resource utilization, and enhanced operational resilience, establishing a robust AI-driven security ecosystem for modern financial cloud environments.References
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