Data-Driven AI-Powered Risk Detection and Re-Architecture Framework for SAP Cloud Systems: Integrating Machine Learning and Artificial Neural Networks for Scalable Security
DOI:
https://doi.org/10.15662/IJEETR.2024.0605003Keywords:
Data-Driven Security, SAP Cloud Systems, Artificial Intelligence, Artificial Neural Networks, Risk Detection, Cloud Re-Architecture, Ethical AI, Scalable Security, Anomaly Detection, Enterprise Risk Management, Cybersecurity AutomationAbstract
The increasing complexity of SAP cloud environments demands intelligent, adaptive, and data-centric approaches to mitigate evolving cyber risks. This paper introduces a Data-Driven AI-Powered Risk Detection and Re-Architecture Framework (DD-AI-RDRAF) designed to enhance security, scalability, and system resilience within large-scale SAP cloud infrastructures. The framework employs Machine Learning (ML) and Artificial Neural Networks (ANNs) to analyze heterogeneous data sources—including system logs, performance metrics, and access patterns—to detect anomalies and potential threats in real time.
By leveraging data-driven analytics, the framework not only identifies vulnerabilities but also provides dynamic re-architecture recommendations to optimize SAP configurations and cloud deployments for improved security posture. The integration of neural network-based learning ensures adaptive risk prediction, while an ethical AI governance layer maintains transparency, explainability, and compliance with enterprise security standards.
Experimental validation demonstrates that the DD-AI-RDRAF significantly enhances threat detection accuracy, reduces false positives, and improves response times compared to conventional rule-based and signature-based detection systems. The proposed solution establishes a robust foundation for proactive, scalable, and ethically aligned AI-driven security in modern SAP cloud ecosystems.
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