Intelligent AI and Risk-Aware Analytics for SAP-Centric Cloud and Enterprise Computing Systems

Abstract

The increasing adoption of SAP-centric enterprise systems deployed over cloud and distributed computing environments has significantly enhanced operational efficiency while simultaneously introducing complex security, risk, and performance challenges. This paper presents an intelligent AI and risk-aware analytics framework designed for SAP-centric cloud and enterprise computing systems. The proposed approach integrates machine learning models with SAP transactional data, system logs, network telemetry, and business process metrics to deliver proactive risk identification and analytics-driven decision support. AI techniques are employed to detect anomalies, predict operational and cyber risks, and analyze cross-process dependencies within interconnected enterprise workflows. The framework incorporates security-aware analytics, access governance, and policy-based controls to safeguard sensitive enterprise data while maintaining system scalability and reliability. Cloud-native deployment ensures adaptability across hybrid and multi-cloud SAP landscapes. Experimental evaluation demonstrates improved risk detection accuracy, faster response times, and enhanced resilience compared to traditional rule-based enterprise monitoring approaches. The results validate the effectiveness of intelligent AI-driven analytics in supporting secure, reliable, and data-driven enterprise computing operations.