Cloud-Native AI and ML Architecture for ERP Security in SAP HANA using Multivariate Classification, Neural Networks, and DevSecOps Automation
DOI:
https://doi.org/10.15680/IJCTECE.2023.0602008Keywords:
Cloud-native AI, Machine learning, ERP security, SAP HANA, Multivariate classification, Neural networks, DevSecOps automationAbstract
Enterprise Resource Planning (ERP) systems in modern organizations, particularly SAP HANA, are increasingly targeted by sophisticated cyber threats due to their critical role in managing business operations and sensitive data. This paper presents a cloud-native AI and ML architecture designed to enhance ERP security through the integration of multivariate classification, neural networks, and DevSecOps automation. The proposed framework leverages multivariate classification algorithms to detect complex, correlated threat patterns across transactional, operational, and user-behavior datasets. Neural networks provide deep learning–driven anomaly detection and predictive threat modeling, while DevSecOps practices enable continuous monitoring, automated compliance enforcement, and rapid incident response in cloud environments. Cloud-native deployment ensures scalability, high availability, and low-latency processing of large-scale ERP datasets. Experimental evaluation demonstrates improved detection accuracy, reduced response times, and enhanced ERP security posture compared to conventional approaches. This architecture provides a robust, scalable, and adaptive solution for organizations seeking to secure SAP HANA ERP systems while maintaining operational efficiency, regulatory compliance, and business continuity. The study underscores the value of combining AI, ML, and DevSecOps in cloud-native environments to achieve proactive and intelligent cyber defense for enterprise systems.
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