An AI-Enabled Cloud Security Intelligence Framework for Large-Scale SAP Fraud Detection and Dynamic Threat Prevention

Main Article Content

Jackson Matthew Fairchild Moore

Abstract

The increasing adoption of SAP workloads in cloud environments has introduced new security challenges related to fraud, insider threats, and large-scale attack surfaces. Traditional security mechanisms often lack the scalability and adaptability required to detect sophisticated fraud patterns and dynamically evolving threats across enterprise cloud infrastructures. This paper presents an AI-Enabled Cloud Security Intelligence Framework designed to support large-scale SAP fraud detection and dynamic threat prevention in cloud-based enterprise environments.

The proposed framework integrates artificial intelligence techniques with cloud-native security services to continuously analyze transactional, behavioral, and system-level data generated by SAP applications. By leveraging scalable cloud processing and intelligent risk assessment, the framework enables real-time anomaly detection, adaptive threat prioritization, and proactive security responses. The architecture supports multi-tenant isolation, high availability, and secure data processing at scale, making it suitable for enterprise deployments handling massive data volumes. The framework enhances security visibility, reduces detection latency, and improves resilience against evolving fraud and cyber threats, demonstrating the effectiveness of AI-driven cloud security intelligence for protecting modern SAP systems.

Article Details

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How to Cite

An AI-Enabled Cloud Security Intelligence Framework for Large-Scale SAP Fraud Detection and Dynamic Threat Prevention. (2024). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(Special Issue 1), 13-21. https://doi.org/10.15662/IJRPETM.2024.0705803

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