Intrusion-Governed AI-Enabled Cloud Framework for Secure Multiparty Healthcare IT and Privacy-Aware Digital Advertising
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The convergence of healthcare information systems and digital advertising platforms has intensified the need for secure, privacy-aware, and intelligent cloud infrastructures capable of supporting multiparty data exchange. Traditional cloud security mechanisms often lack the adaptability required to address evolving cyber threats, regulatory constraints, and cross-domain data sharing requirements. This paper proposes an intrusion-governed, AI-enabled cloud framework designed to secure multiparty healthcare IT environments while enabling privacy-aware digital advertising workflows. The framework integrates AI-driven intrusion detection with governed data platforms to continuously monitor network behavior, detect anomalies, and enforce access controls across distributed stakeholders. Secure APIs and policy-based data governance mechanisms ensure compliant data sharing, consent management, and interoperability between healthcare systems and advertising platforms. Experimental evaluation and architectural analysis demonstrate improved threat detection accuracy, reduced data exposure risk, and enhanced compliance with healthcare and data protection regulations, making the framework suitable for large-scale, security-sensitive cloud deployments.
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1. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
2. Buyya, R., Yeo, C. S., & Venugopal, S. (2008). Market-oriented cloud computing: Vision, hype, and reality for delivering IT services as computing utilities. IEEE Transactions on Services Computing, 1(1), 14–25.
3. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
4. Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
5. Thumala, S. R., Mane, V., Patil, T., Tambe, P., & Inamdar, C. (2025, June). Full Stack Video Conferencing App using TypeScript and NextJS. In 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 1285-1291). IEEE.
6. Hashizume, K., Rosado, D. G., Fernández-Medina, E., & Fernández, E. B. (2013). An analysis of security issues for cloud computing. Journal of Internet Services and Applications, 4(1), 5.
7. Madabathula, L. (2025). Dynamic Data Orchestration: Enhancing Business Intelligence with Azure Data Factory. IJSAT-International Journal on Science and Technology, 16(1).
8. Rajurkar, P. (2023). Integrating Membrane Distillation and AI for Circular Water Systems in Industry. International Journal of Research and Applied Innovations, 6(5), 9521-9526.
9. Karnam, A. (2024). Next-Gen Observability for SAP: How Azure Monitor Enables Predictive and Autonomous Operations. International Journal of Computer Technology and Electronics Communication, 7(2), 8515–8524. https://doi.org/10.15680/IJCTECE.2024.0702006
10. Mahajan, N. (2025). GOVERNANCE OF CROSS-FUNCTIONAL DELIVERY IN SCALABLE MULTI-VENDOR AGILE TRANSFORMATIONS. International Journal of Applied Mathematics, 38(2s), 156-167.
11. Thambireddy, S. (2021). Enhancing Warehouse Productivity through SAP Integration with Multi-Model RF Guns. International Journal of Computer Technology and Electronics Communication, 4(6), 4297-4303.
12. Shashank, P. S. R. B., Anand, L., & Pitchai, R. (2024, December). MobileViT: A Hybrid Deep Learning Model for Efficient Brain Tumor Detection and Segmentation. In 2024 International Conference on Progressive Innovations in Intelligent Systems and Data Science (ICPIDS) (pp. 157-161). IEEE.
13. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
14. Rahanuma, T., Sakhawat Hussain, T., Md Manarat Uddin, M., & Md Ashiqul, I. (2024). Healthcare Investment Trends: A Post-COVID Capital Market Analysis Investigating How Public Health Crises Reshape Healthcare Venture Capital and M&A Activity. American Journal of Technology Advancement, 1(1), 51-79.
15. Chandramohan, A. (2017). Exploring and overcoming major challenges faced by IT organizations in business process improvement of IT infrastructure in Chennai, Tamil Nadu. International Journal of Mechanical Engineering and Technology, 8(12), 254.
16. Paul, D., Poovaiah, S. A. D., Nurullayeva, B., Kishore, A., Tankani, V. S. K., & Meylikulov, S. (2025, July). SHO-Xception: An Optimized Deep Learning Framework for Intelligent Intrusion Detection in Network Environments. In 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3) (pp. 1-6). IEEE.
17. Ramakrishna, S. (2023). Cloud-Native AI Platform for Real-Time Resource Optimization in Governance-Driven Project and Network Operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6282-6291.
18. Vasugi, T. (2023). An Intelligent AI-Based Predictive Cybersecurity Architecture for Financial Workflows and Wastewater Analytics. International Journal of Computer Technology and Electronics Communication, 6(5), 7595-7602.
19. Nagarajan, G. (2023). AI-Integrated Cloud Security and Privacy Framework for Protecting Healthcare Network Information and Cross-Team Collaborative Processes. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6292-6297.
20. Sivaraju, P. S. (2023). Thin client and service proxy architectures for real-time staffing systems in distributed operations. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(6), 9510-9515.
21. Meka, S. (2025). Fortifying Core Services: Implementing ABA Scopes to Secure Revenue Attribution Pipelines. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(2), 11794-11801.
22. Akter Tohfa, N., Alim, M. A., Arif, M. H., Rahman, M. R., Rahman, M., Rasul, I., & Hossen, M. S. (2025). Machine learning–enabled anomaly detection for environmental risk management in banking. World Journal of Advanced Research and Reviews, 28(3), 1674–1682. https://doi.org/10.30574/wjarr.2025.28.3.4259
23. 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.
24. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (NIST Special Publication 800-145). National Institute of Standards and Technology.
25. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
26. Bussu, V. R. R. (2023). Governed Lakehouse Architecture: Leveraging Databricks Unity Catalog for Scalable, Secure Data Mesh Implementation. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6298-6306.
27. Natta P K. AI-Driven Decision Intelligence: Optimizing Enterprise Strategy with AI-Augmented Insights[J]. Journal of Computer Science and Technology Studies, 2025, 7(2): 146-152.
28. S. Kabade and A. Sharma, “Intelligent Automation in Pension Service Purchases with AI and Cloud Integration for Operational Excellence,” Int. J. Adv. Res. Sci. Commun. Technol., pp. 725–735, Dec. 2024, doi: 10.48175/IJARSCT-14100J.
29. Kusumba, S. (2025). Driving US Enterprise Agility: Unifying Finance, HR, and CRM with an Integrated Analytics Data Warehouse. IPHO-Journal of Advance Research in Science And Engineering, 3(11), 56-63.
30. Kumar, R. K. (2024). Real-time GenAI neural LDDR optimization on secure Apache–SAP HANA cloud for clinical and risk intelligence. IJEETR, 8737–8743. https://doi.org/10.15662/IJEETR.2024.0605006
31. Scarfone, K., & Mell, P. (2007). Guide to intrusion detection and prevention systems (IDPS) (NIST Special Publication 800-94). National Institute of Standards and Technology.
32. Archana, R., & Anand, L. (2023, September). Ensemble Deep Learning Approaches for Liver Tumor Detection and Prediction. In 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 325-330). IEEE.
33. Kasireddy, J. R. (2023). Optimizing multi-TB market data workloads: Advanced partitioning and skew mitigation strategies for Hive and Spark on EMR. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 6982–6990. https://doi.org/10.15680/IJCTECE.2023.0603005
34. Singh, A. (2023). Integrating Fiber Broadband and 5G Network: Synergies and Challenges. https://www.researchgate.net/profile/Abhishek-Singh-679/publication/388757728_Integrating_Fiber_Broadband_and_5G_Network_Synergies_and_Challenges/links/687cff484f72461c714f8099/Integrating-Fiber-Broadband-and-5G-Network-Synergies-and-Challenges.pdf
35. 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.
36. Zhang, Q., Cheng, L., & Boutaba, R. (2011). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18.