(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2022)
Healthcare organizations increasingly rely on cloud-based ERP systems to manage clinical, operational, and financial workflows. However, the growing volume of sensitive data and evolving cyber threats present significant challenges for security, compliance, and operational efficiency. This paper presents a next-generation cloud security and analytics framework for healthcare ERP systems, integrating machine learning (ML) and DevOps automation to deliver intelligent, real-time risk detection and system optimization. The proposed architecture leverages ML algorithms for anomaly detection, predictive threat modeling, and financial risk assessment, while cloud-native deployment ensures scalability, high availability, and secure processing of large datasets. DevOps automation enables continuous monitoring, rapid patch deployment, and streamlined compliance enforcement across distributed healthcare ERP environments. Experimental evaluation demonstrates improved detection of cybersecurity threats, faster response times, and enhanced decision-making capabilities compared to traditional ERP security mechanisms. By combining advanced analytics, cloud scalability, and automated DevOps practices, the framework provides a robust, adaptive, and explainable solution for securing healthcare ERP systems, optimizing operational workflows, and mitigating financial and cyber risks. This study highlights the potential of AI and cloud-native DevOps integration to transform security and analytics practices in modern healthcare enterprises.