SAP HANA–Driven Real-Time AI Cloud DevOps Architecture for Scalable ML, DL, and ERP-Integrated Cybersecurity Threat Detection

Main Article Content

Matteo Francesco De Luca Marino

Abstract

Modern enterprises increasingly rely on cloud-based infrastructures and ERP systems to support large-scale operations, real-time analytics, and secure digital workflows. However, the integration of AI, machine learning (ML), and deep learning (DL) into these systems presents challenges in scalability, operational efficiency, and cybersecurity resilience. This paper proposes a SAP HANA–driven real-time AI Cloud DevOps architecture designed to address these challenges by combining high-performance in-memory computing with intelligent DevOps pipelines and ERP integration. The framework leverages ML and DL models for predictive analytics, anomaly detection, and threat intelligence to identify and mitigate cybersecurity risks in real time. ERP integration ensures seamless interoperability across enterprise processes, while DevOps automation enables continuous deployment, monitoring, and rapid response to emerging threats. The proposed architecture is scalable, adaptive, and capable of enhancing operational efficiency, security, and reliability in enterprise environments handling large volumes of sensitive transactional and operational data. Experimental evaluations demonstrate the framework’s effectiveness in improving threat detection accuracy, reducing response times, and optimizing resource utilization.

Article Details

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Articles

How to Cite

SAP HANA–Driven Real-Time AI Cloud DevOps Architecture for Scalable ML, DL, and ERP-Integrated Cybersecurity Threat Detection. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9699-9706. https://doi.org/10.15662/IJRPETM.2023.0606010

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