Secure Software Testing and Validation Frameworks for SAP-Centric Cloud-Native Healthcare Machine Learning Systems Managing PII and Regulated Data
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Abstract
Healthcare organizations increasingly deploy machine learning (ML) systems within cloud-native enterprise environments to improve diagnostics, operational efficiency, and patient outcomes. However, these systems process highly sensitive personally identifiable information (PII) and regulated health data, requiring rigorous software testing, validation, and compliance assurance. This paper proposes a secure software testing and validation framework tailored for SAP-centric cloud-native healthcare ML systems. The framework integrates automated testing pipelines, compliance-aware validation, privacy-preserving techniques, and continuous monitoring mechanisms to ensure secure, reliable, and regulatory-compliant deployment of ML-enabled healthcare applications
The proposed architecture combines SAP enterprise platforms, cloud infrastructure, and ML services within a zero-trust security model. It incorporates static and dynamic testing, model validation, data privacy verification, and compliance auditing across the software lifecycle. DevSecOps practices and automated compliance testing ensure that systems meet healthcare regulations and data protection standards. The framework also integrates explainable AI and bias testing to enhance transparency and ethical decision-making
Evaluation results demonstrate improved detection of vulnerabilities, enhanced model reliability, and reduced compliance risks. The framework supports continuous integration and continuous deployment (CI/CD) pipelines, enabling secure and scalable deployment of ML models in healthcare environments. By integrating security, testing, and compliance mechanisms, the proposed approach enhances trust and resilience in SAP-centric healthcare systems handling sensitive patient data
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1. Behl, A., & Behl, K. (2021). Cybersecurity and cyberwar: What everyone needs to know. Oxford University Press.
2. Gangina, P. (2025). Modernizing legacy applications for cloud: Strategies and lessons learned. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(5), 11495–11501.
3. NIST. (2020). Zero trust architecture (SP 800-207). National Institute of Standards and Technology.
4. Ransbotham, S., Kiron, D., & Gerbert, P. (2021). Artificial intelligence in business and healthcare. MIT Sloan Management Review.
5. Sarker, I. H. (2021). Machine learning for intelligent cybersecurity analytics. Journal of Big Data, 8(1), 1–27.
6. Natta, P. K. (2024). Autonomous cloud optimization leveraging AI-augmented decision frameworks. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7817–7829. https://doi.org/10.15662/IJEETR.2024.0602005
7. Sharma, S., & Chen, K. (2022). Privacy-preserving cloud architectures for healthcare systems. IEEE Cloud Computing, 9(3), 40–50.
8. Sriramoju, S. (2025). Implementing CI/CD Pipelines for MuleSoft APIs Using Jenkins, GitHub, and Azure DevOps. Journal of Computer Science and Technology Studies, 7(8), 77-82.
9. Rajasekharan, R. (2024). The evolving role of Oracle Cloud DBAs in the AI era. International Journal of Computer Technology and Electronics Communication (IJCTEC), 7(6), 9866–9879.
10. Kasireddy, J. R. (2025). The cloud cost-optimization flywheel: A systematic approach to reducing infrastructure waste without compromising delivery velocity. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 8(2), 16075–16087
11. Joseph, J. (2023). DiffusionClaims–PHI-Safe Synthetic Claims for Robust Anomaly Detection. International Journal of Computer Technology and Electronics Communication, 6(3), 6958-6973.
12. Thumala, S. R., & Pillai, B. S. (2024). Cloud Cost Optimization Methodologies for Cloud Migrations. International Journal of Intelligent Systems and Applications in Engineering.
13. Ferdousi, J., Shokran, M., & Islam, M. S. (2026). Designing Human–AI Collaborative Decision Analytics Frameworks to Enhance Managerial Judgment and Organizational Performance. Journal of Business and Management Studies, 8(1), 01-19.
14. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
15. Panchakarla, S. K. (2025). Context-aware rule engines for pricing and claims processing in healthcare platforms. International Journal of Computer Technology and Electronics Communication, 8(4), 11087–11091.
16. Kathiresan, G. (2025). Real-time data ingestion and stream processing for AI applications in cloud-native environments. International Journal of Cloud Computing (QITP-IJCC). QIT Press, Volume 5, Issue 2, 2025, pp.12-23
17. Mudunuri, P. R. (2025). Automation, compliance, and public health reliability in biomedical infrastructure. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 11086–11093.
18. Navandar, P. (2025). AI Based Cybersecurity for Internet of Things Networks via Self-Attention Deep Learning and Metaheuristic Algorithms. International Journal of Research and Applied Innovations, 8(3), 13053-13077.
19. Keezhadath, A. A., & Amarapalli, L. (2024). Ensuring Data Integrity in Pharmaceutical Quality Systems: A Risk-Based Approach. Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930), 1(1), 83-104.
20. Genne, S. (2025). Engineering Secure Financial Portals: A Case Study in Credit Line Increase Process Digitization. Journal Of Multidisciplinary, 5(7), 563-570.
21. Potdar, A., Gottipalli, D., Ashirova, A., Kodela, V., Donkina, S., & Begaliev, A. (2025, July). MFO-AIChain: An Intelligent Optimization and Blockchain-Backed Architecture for Resilient and Real-Time Healthcare IoT Communication. In 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3) (pp. 1-6). IEEE.
22. Panda, M. R., Musunuru, M. V., & Sardana, A. (2025). Federated Reinforcement Learning for Adaptive Fraud Behavior Analytics in Digital Banking. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 4(3), 90-96.
23. Singh, A. (2025). Intent-Based Networking in Multi-Cloud Environments. Journal of Engineering and Applied Sciences Technology, 7(2), 1-7.
24. Khokrale, R. (2025). Cybersecurity in ERP-Integrated Supply Chains: Risks and Mitigation Strategies. The Eastasouth Journal of Information System and Computer Science, 3(02), 271-291.
25. Surisetty, L. S. (2024). Improving Disease Detection Accuracy with Al and Secure Data Exchange through API Gateways. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(3), 10346-10354.
26. Chivukula, V. (2020). Use of multiparty computation for measurement of ad performance without exchange of personally identifiable information (PII). International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1546-1551.
27. Kusumba, S. (2025). Modernizing US Healthcare Financial Systems: A Unified HIGLAS Data Lakehouse for National Efficiency and Accountability. International Journal of Computing and Engineering, 7(12), 24-37.
28. Gopinathan, V. R. (2024). Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking. International Journal of Computer Technology and Electronics Communication, 7(6), 9837-9845.
29. Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.