Machine Learning-Enabled Predictive Security and Governance Frameworks for SAP-Integrated Cloud-Native Enterprise Systems
DOI:
https://doi.org/10.15662/IJRAI.2023.0605011Keywords:
Machine Learning, Predictive Security, SAP Enterprise Systems, Cloud-Native Architecture, Cybersecurity Analytics, Identity Governance, Anomaly Detection, Enterprise Risk Management, Intelligent Monitoring Systems, Digital Enterprise SecurityAbstract
The rapid transformation of enterprise digital ecosystems has led organizations to increasingly adopt cloud-native architectures integrated with enterprise resource planning platforms such as SAP. While these advancements provide greater scalability, operational efficiency, and real-time analytics capabilities, they also introduce complex security and governance challenges. Enterprise environments today must manage large volumes of sensitive data, distributed workloads, and multiple access points across hybrid and multi-cloud infrastructures. As a result, organizations require intelligent and predictive security mechanisms capable of proactively detecting threats, ensuring regulatory compliance, and maintaining governance standards across enterprise systems. Machine learning technologies have emerged as powerful tools for analyzing large-scale operational data and identifying patterns associated with potential cyber threats or governance violations. This research proposes a machine learning-enabled predictive security and governance framework designed specifically for SAP-integrated cloud-native enterprise environments. The framework combines AI-driven anomaly detection models, identity-centric governance policies, and automated monitoring mechanisms to improve enterprise security posture and operational reliability. The study evaluates the proposed framework through simulated enterprise infrastructure environments that generate large-scale operational logs, user activity records, and system transaction data. Experimental results demonstrate that predictive machine learning models significantly enhance threat detection accuracy and reduce response times compared to traditional rule-based security systems. Furthermore, the framework improves enterprise governance by enabling continuous monitoring of access control policies and compliance requirements. The research findings highlight the potential of integrating machine learning with cloud-native enterprise architectures to create resilient, scalable, and intelligent digital ecosystems capable of addressing modern cybersecurity and governance challenges.
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