A Privacy-Preserving LLM-Integrated DevOps and Testing Framework for ERP-Enabled Rural Healthcare Cloud Platforms with Intelligent Fraud Prevention
DOI:
https://doi.org/10.15662/IJEETR.2024.0606009Keywords:
Privacy-preserving AI, Large Language Models, DevOps, ERP systems, Rural healthcare, Cloud computing, Fraud prevention, Cybersecurity, Machine learning, Data analytics, Threat detection, Secure automation, Healthcare information systems, Real-time monitoring, AI-driven testingAbstract
Rural healthcare systems increasingly rely on ERP-enabled cloud platforms to streamline operations, improve patient outcomes, and support real-time decision-making. However, these environments face critical challenges related to data privacy, system reliability, and sophisticated fraud attempts targeting financial and clinical workflows. This paper proposes a privacy-preserving DevOps and testing framework integrated with Large Language Models (LLMs) to enhance security, automation, and operational efficiency across rural healthcare infrastructures. The framework leverages secure LLM agents for automated code review, anomaly detection, intelligent test generation, and continuous monitoring while enforcing strict data minimization and encryption protocols to safeguard sensitive patient information. Additionally, the system incorporates AI-driven fraud prevention mechanisms using machine learning, behavioral analytics, and rule-based engines within ERP and cloud layers to detect credit card misuse, insurance manipulation, and identity-based fraud in real time. By combining DevOps automation, privacy-by-design principles, and intelligent threat detection, the proposed solution delivers a scalable, resilient, and secure architecture tailored for resource-constrained rural healthcare environments.
References
1. Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software engineering for machine learning: A case study. _Proceedings of the 41st International Conference on Software Engineering_.
2. Ramakrishna, S. (2022). AI-augmented cloud performance metrics with integrated caching and transaction analytics for superior project monitoring and quality assurance. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5647–5655. https://doi.org/10.15662/IJEETR.2022.0406005
3. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J., & Dennison, D. (2015). Hidden technical debt in machine learning systems. _Communications of the ACM, 59_(11), 80–88.
4. Vasugi, T. (2023). AI-empowered neural security framework for protected financial transactions in distributed cloud banking ecosystems. International Journal of Advanced Research in Computer Science & Technology, 6(2), 7941–7950. https://doi.org/0.15662/IJARCST.2023.0602004
5. Rahman, M., Arif, M. H., Alim, M. A., Rahman, M. R., &Hossen, M. S. (2021). Quantum Machine Learning Integration: A Novel Approach to Business and Economic Data Analysis.
6. Hardial Singh, “Securing High-Stakes DigitalTransactions: A Comprehensive Study on Cybersecurity and Data Privacy in Financial Institutions”, Science, Technology and Development, Volume XII Issue X OCTOBER 2023.
7. Islam, M. S., Shokran, M., &Ferdousi, J. (2024). AI-Powered Business Analytics in Marketing: Unlock Consumer Insights for Competitive Growth in the US Market. Journal of Computer Science and Technology Studies, 6(1), 293-313.
8. Surampudi, Y., Kondaveeti, D., &Pichaimani, T. (2023). A Comparative Study of Time Complexity in Big Data Engineering: Evaluating Efficiency of Sorting and Searching Algorithms in Large-Scale Data Systems. Journal of Science & Technology, 4(4), 127-165.
9. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
10. Sumaya, A. I., Forhad, S., Al Rafi, M., Rahman, H., Bhuyan, M. H., &Tareq, Q. (2024, September). Comparative Analysis of AlexNet, GoogLeNet, VGG19, ResNet50, and ResNet101 for Improved Plant Disease Detection Through Convolutional Neural Networks. In 2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings) (pp. 1-6). IEEE.
11. Devan, M., Althati, C., &Perumalsamy, J. (2023). Real-Time Data Analytics for Fraud Detection in Investment Banking Using AI and Machine Learning: Techniques and Case Studies. Cybersecurity and Network Defense Research, 3(1), 25-56.
12. Thangavelu, K., Sethuraman, S., &Hasenkhan, F. (2021). AI-Driven Network Security in Financial Markets: Ensuring 100% Uptime for Stock Exchange Transactions. American Journal of Autonomous Systems and Robotics Engineering, 1, 100-130.
13. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. _Foundations and Trends in Theoretical Computer Science, 9_(3–4), 211–407.
14. Sivaraju, P. S. (2022). Enterprise-Scale Data Center Migration and Consolidation: Private Bank's Strategic Transition to HP Infrastructure. International Journal of Computer Technology and Electronics Communication, 5(6), 6123-6134.
15. Vijayaboopathy, V., &Dhanorkar, T. (2021). LLM-Powered Declarative Blueprint Synthesis for Enterprise Back-End Workflows. American Journal of Autonomous Systems and Robotics Engineering, 1, 617-655.
16. Kumar, R. K. (2022). AI-driven secure cloud workspaces for strengthening coordination and safety compliance in distributed project teams. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8075–8084. https://doi.org/10.15662/IJRAI.2022.0506017
17. Fielding, R. T., & Taylor, R. N. (2000). Architectural styles and the design of network-based software architectures. _PhD Thesis, University of California, Irvine_.
18. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., &Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3(5), 44–53. https://doi.org/10.46632/daai/3/5/7
19. Muthusamy, M. (2024). Cloud-Native AI metrics model for real-time banking project monitoring with integrated safety and SAP quality assurance. International Journal of Research and Applied Innovations (IJRAI), 7(1), 10135–10144. https://doi.org/10.15662/IJRAI.2024.0701005
20. Archana, R., &Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
21. Lipton, Z. C. (2018). The mythos of model interpretability. _Communications of the ACM, 61_(10), 36–43.
22. Nagarajan, G. (2022). Optimizing project resource allocation through a caching-enhanced cloud AI decision support system. International Journal of Computer Technology and Electronics Communication, 5(2), 4812–4820. https://doi.org/10.15680/IJCTECE.2022.0502003
23. Pasumarthi, A., & Joyce, S. SABRIX FOR SAP: A COMPARATIVE ANALYSIS OF ITS FEATURES AND BENEFITS. https://www.researchgate.net/publication/395447894_International_Journal_of_Engineering_Technology_Research_Management_SABRIX_FOR_SAP_A_COMPARATIVE_ANALYSIS_OF_ITS_FEATURES_AND_BENEFITS
24. Navandar, P. (2021). Developing advanced fraud prevention techniques using data analytics and ERP systems. International Journal of Science and Research (IJSR), 10(5), 1326–1329. https://dx.doi.org/10.21275/SR24418104835 https://www.researchgate.net/profile/Pavan-Navandar/publication/386507190_Developing_Advanced_Fraud_Prevention_Techniquesusing_Data_Analytics_and_ERP_Systems/links/675a0ecc138b414414d67c3c/Developing-Advanced-Fraud-Prevention-Techniquesusing-Data-Analytics-and-ERP-Systems.pdf
25. Humble, J., Molesky, J., & O'Reilly, J. (2010). Release It!: Design and Deploy Production-Ready Software. _Pragmatic Bookshelf_.
26. Kumar, S. N. P. (2022). Improving Fraud Detection in Credit Card Transactions Using Autoencoders and Deep Neural Networks (Doctoral dissertation, The George Washington University).
27. Balaji, K. V., &Sugumar, R. (2022, December). A Comprehensive Review of Diabetes Mellitus Exposure and Prediction using Deep Learning Techniques. In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (Vol. 1, pp. 1-6). IEEE.
28. Navandar, P. (2021). Developing advanced fraud prevention techniques using data analytics and ERP systems. International Journal of Science and Research (IJSR), 10(5), 1326–1329. https://dx.doi.org/10.21275/SR24418104835 https://www.researchgate.net/profile/Pavan-Navandar/publication/386507190_Developing_Advanced_Fraud_Prevention_Techniquesusing_Data_Analytics_and_ERP_Systems/links/675a0ecc138b414414d67c3c/Developing-Advanced-Fraud-Prevention-Techniquesusing-Data-Analytics-and-ERP-Systems.pdf
29. Suchitra, R. (2023). Cloud-Native AI model for real-time project risk prediction using transaction analysis and caching strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8006–8013. https://doi.org/10.15662/IJRPETM.2023.0601002
30. AnujArora, “The Future of Cybersecurity: Trends and Innovations Shaping Tomorrow's Threat Landscape”, Science, Technology and Development, Volume XI Issue XII DECEMBER 2022.
31. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
32. Kandula, N. (2023). Evaluating Social Media Platforms A Comprehensive Analysis of Their Influence on Travel Decision-Making. J Comp SciAppl Inform Technol, 8(2), 1-9.
