Enterprise Deployment of CNN-Based AI Models for Secure and Privacy-Aware Healthcare Applications
DOI:
https://doi.org/10.15662/IJEETR.2025.0705010Keywords:
Convolutional Neural Networks, Healthcare AI, Enterprise Deployment, Data Privacy, Secure AI Systems, Medical Imaging, Federated Learning, HIPAA ComplianceAbstract
The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly through the use of Convolutional Neural Networks (CNNs) for medical image analysis, disease prediction, and clinical decision support. While CNN-based models demonstrate exceptional performance in tasks such as radiology image classification, pathology detection, and patient risk stratification, deploying these models at the enterprise level introduces critical challenges related to data security, patient privacy, regulatory compliance, and system scalability. Healthcare data is highly sensitive and subject to strict regulations such as HIPAA and GDPR, necessitating privacy-aware AI architectures that ensure confidentiality without compromising performance. This paper explores the enterprise deployment of CNN-based AI models within secure and privacy-preserving healthcare environments. It examines architectural frameworks, data governance strategies, secure model training techniques, and deployment methodologies that align with real-world clinical workflows. Furthermore, the study highlights emerging technologies such as federated learning, secure enclaves, and encryption-based inference as viable solutions to privacy risks. By synthesizing existing research and proposing a structured deployment methodology, this paper aims to guide healthcare organizations and AI practitioners in implementing CNN-based systems that are robust, scalable, secure, and compliant with regulatory standards.
References
1. Kusumba, S. (2024). Accelerating AI and Data Strategy Transformation: Integrating Systems, Simplifying Financial Operations Integrating Company Systems to Accelerate Data Flow and Facilitate Real-Time Decision-Making. The Eastasouth Journal of Information System and Computer Science, 2(02), 189-208.
2. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
3. Raj, A. M. A., Rajendran, S., & Vimal, G. S. A. G. (2024). Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection. Bulletin of Electrical Engineering and Informatics, 13(3), 1935-1942.
4. Ananth, S., & Saranya, A. (2016, January). Reliability enhancement for cloud services-a survey. In 2016 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-7). IEEE.
5. Ramidi, M. (2023). Implementing privacy-focused data sharing frameworks for mobile healthcare communication. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8746–8757.
6. Panda, M. R., Devi, C., & Dhanorkar, T. (2024). Generative AI-Driven Simulation for Post-Merger Banking Data Integration. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 7(01), 339-350.
7. Genne, S. (2022). Designing accessibility-first enterprise web platforms at scale. International Journal of Research and Applied Innovations (IJRAI), 5(5), 7679–7690.
8. 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.
9. Surisetty, L. S. (2021). Zero-Trust Data Fabrics: A Policy-Driven Model for Secure Cross-Cloud Healthcare and Financial Data Exchanges. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(2), 4548-4556.
10. Rao, N. S., Shanmugapriya, G., Vinod, S., & Mallick, S. P. (2023, March). Detecting human behavior from a silhouette using convolutional neural networks. In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS) (pp. 943-948). IEEE.
11. Sudakara, B. B. (2023). Integrating Cloud-Native Testing Frameworks with DevOps Pipelines for Healthcare Applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9309-9316.
12. Alam, M. K., Mahmud, M. A., & Islam, M. S. (2024). The AI-Powered Treasury: A Data-Driven Approach to managing America’s Fiscal Future. Journal of Computer Science and Technology Studies, 6(2), 236-256.
13. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
14. Chennamsetty, C. S. (2022). Hardware-Software Co-Design for Sparse and Long-Context AI Models: Architectural Strategies and Platforms. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(5), 7121-7133.
15. 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.
16. Zerine, I., Islam, M. S., Ahmad, M. Y., Islam, M. M., & Biswas, Y. A. (2023). AI-Driven Supply Chain Resilience: Integrating Reinforcement Learning and Predictive Analytics for Proactive Disruption Management. Business and Social Sciences, 1(1), 1-12.
17. Vimal Raja, G. (2025). Context-Aware Demand Forecasting in Grocery Retail Using Generative AI: A Multivariate Approach Incorporating Weather, Local Events, and Consumer Behaviour. International Journal of Innovative Research in Science Engineering and Technology (Ijirset), 14(1), 743-746.
18. Anumula, S. R. (2022). Transparent and auditable decision-making in enterprise platforms. International Journal of Research and Applied Innovations (IJRAI), 5(5), 7691–7702. https://doi.org/10.15662/IJRAI.2022.0505007
19. 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.
20. Ponugoti, M. (2022). Integrating full-stack development with regulatory compliance in enterprise systems architecture. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(2), 6550–6563.
21. Sriramoju, S. (2024). Optimizing data flow: A unified approach for product, pricing, and revenue sync in enterprise systems. International Journal of Engineering & Extended Technologies Research, 6(1), 7492–7503.
22. Raju, S., & Sindhuja, D. (2024). Transparent encryption for external storage media with mobile-compatible key management by Crypto Ciphershield. PatternIQ Mining, 1(3), 12-24.
23. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
24. Gangina, P. (2023). Service mesh implementation strategies for zero-downtime migrations in production environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(5), 7208–7220.
25. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
26. Chivukula, V. (2020). IMPACT OF MATCH RATES ON COST BASIS METRICS IN PRIVACY-PRESERVING DIGITAL ADVERTISING. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 3(4), 3400-3405.
27. Natta, P. K. (2024). Closed-loop AI frameworks for real-time decision intelligence in enterprise environments. International Journal of Humanities and Information Technology, 6(3). https://doi.org/10.21590/ijhit.06.03.05
28. Kota, R. K., Keezhadath, A. A., & Kondaveeti, D. (2021). AI-Driven Predictive Analytics in Retail: Enhancing Customer Engagement and Revenue Growth. American Journal of Autonomous Systems and Robotics Engineering, 1, 234-274.
29. Sundaresh, G., Ramesh, S., Malarvizhi, K., & Nagarajan, C. (2025, April). Artificial Intelligence Based Smart Water Quality Monitoring System with Electrocoagulation Technique. In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-6). IEEE.
30. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
31. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
32. Ananth, S., Radha, D. K., Prema, D. S., & Nirajan, K. (2019). Fake news detection using convolution neural network in deep learning. International Journal of Innovative Research in Computer and Communication Engineering, 7(1), 49-63.
33. Gopinathan, V. R. (2024). Secure Explainable AI on Databricks–SAP Cloud for Risk-Sensitive Healthcare Analytics and Swarm-Based QoS Control. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8452-8459.
34. Sudakara, B. B. (2023). Integrating Cloud-Native Testing Frameworks with DevOps Pipelines for Healthcare Applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9309-9316.
