Secure-by-Design Cloud AI and ML Framework for Healthcare SAP Systems on Microsoft Azure

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Published: 2022-07-15
DOI: https://doi.org/10.15662/IJRPETM.2022.0504008
Keywords:
Cloud AI, Machine Learning, Healthcare Information Systems, Secure-by-Design Architecture, Secure-by-Design ArchitectureMicrosoft Azure, Data Privacy, MLOps, Healthcare Compliance, Cloud Security

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Daan Pieter De Vries
Independent Researcher, Netherland

Abstract

Healthcare organizations increasingly rely on SAP systems to manage critical clinical, financial, and operational data, making security, privacy, and compliance paramount. This paper presents a secure-by-design cloud-based artificial intelligence (AI) and machine learning (ML) framework for healthcare SAP systems deployed on Microsoft Azure. The proposed architecture integrates native Azure security services, identity and access management, data encryption, and continuous monitoring mechanisms to ensure confidentiality, integrity, and availability of sensitive healthcare information. AI- and ML-driven analytics are leveraged to enhance system intelligence through predictive insights, anomaly detection, and automated risk mitigation while maintaining regulatory compliance with healthcare standards. The framework emphasizes scalable MLOps pipelines, secure data ingestion, and seamless integration with SAP workloads to support real-time decision-making and operational efficiency. Experimental analysis and architectural evaluation demonstrate that the proposed approach improves security posture, system resilience, and performance compared to traditional cloud deployments, making it suitable for modern, large-scale healthcare environments.

Article Details

Issue

Vol. 5 No. 4 (2022): The International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Secure-by-Design Cloud AI and ML Framework for Healthcare SAP Systems on Microsoft Azure. (2022). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7145-1751. https://doi.org/10.15662/IJRPETM.2022.0504008

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