Digital Twin-Driven Cloud Infrastructure Optimization through Predictive AI for SAP Systems and Cyber Resilience

Abstract

The increasing complexity of enterprise cloud environments has intensified the need for intelligent infrastructure optimization, particularly for organizations operating SAP systems that support critical business processes. Digital twin technology, integrated with predictive artificial intelligence (AI), offers a promising approach to improving resource utilization, system performance, operational reliability, and cyber resilience. This study examines how digital twins can create virtual representations of SAP cloud infrastructure to simulate workloads, anticipate performance bottlenecks, predict resource demands, and identify potential cybersecurity threats before they affect production environments. The proposed approach combines infrastructure telemetry, machine learning algorithms, predictive analytics, anomaly detection, and scenario-based simulation to support proactive infrastructure management. Particular emphasis is placed on SAP application workloads, database performance, cloud resource allocation, system availability, and security monitoring. The research methodology adopts a design-oriented framework supported by quantitative performance indicators and comparative evaluation of conventional reactive management and digital twin-driven predictive optimization. Key evaluation dimensions include resource utilization, prediction accuracy, incident detection, recovery time, operational cost, and service availability. The study also considers data governance, model explainability, integration complexity, and the security of digital twin architectures. The proposed framework establishes a foundation for adaptive, risk-aware cloud operations, demonstrating how predictive AI and digital twins can contribute to more efficient SAP infrastructure management while strengthening cyber resilience in increasingly distributed enterprise computing environments.