A Scalable Distributed Cloud–AI Defense Framework for Financial Networks Multivariate Threat Analysis, DevSecOps Security Automation, and SAP ERP–Based Fraud Detection
DOI:
https://doi.org/10.15662/IJEETR.2022.0403003Keywords:
Distributed cloud security, Artificial intelligence, Multivariate threat analysis, DevSecOps automation, CI/CD hardening, Fraud detection, SAP ERP integration, Financial cybersecurity, Anomaly detection, Deep learning, Real-time analytics, Risk management, Cloud-native security, Threat intelligence, Enterprise defense systemsAbstract
Financial networks face increasingly complex cyber threats driven by high-volume transactions, interconnected systems, and sophisticated fraud mechanisms. This paper introduces a scalable distributed Cloud–AI defense framework designed to enhance cybersecurity resilience and fraud detection across financial ecosystems. The proposed architecture integrates multivariate threat analysis, leveraging deep learning and anomaly detection models to identify advanced attack vectors, abnormal transaction behaviors, and fraud patterns in real time. A DevSecOps-driven security automation layer enables continuous integration and continuous delivery (CI/CD) hardening, automated vulnerability remediation, and policy enforcement across cloud-native environments. Additionally, SAP ERP–based fraud detection modules support real-time transactional analytics, cross-ledger validation, and behavioral scoring to uncover hidden fraud patterns within enterprise operations. The distributed cloud foundation ensures scalability, high availability, and secure data exchange across financial institutions. Experimental evaluation demonstrates that the framework significantly improves detection accuracy, reduces incident response time, and strengthens overall risk posture, offering an end-to-end AI-enhanced defense ecosystem for modern financial networks.References
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