AI Driven Secure Enterprise Healthcare Marketing Automation using Machine Learning with Cloud Risk Management
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Abstract
The rapid digital transformation of healthcare has reshaped how organizations engage patients, providers, and stakeholders. Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into enterprise healthcare marketing automation systems to deliver personalized, predictive, and data-driven campaigns. However, the use of sensitive health information requires stringent security, regulatory compliance, and cloud risk management strategies. This study explores an AI-driven secure enterprise healthcare marketing automation framework that integrates ML models with cloud-based risk governance mechanisms. The proposed framework emphasizes data privacy, encryption, regulatory compliance, identity management, and threat detection within cloud infrastructures. It highlights the application of predictive analytics, natural language processing, and recommendation systems to optimize patient engagement while maintaining compliance with healthcare data protection regulations. The research also examines risk mitigation strategies such as zero-trust architecture, continuous monitoring, and automated compliance auditing in multi-cloud environments. By combining AI-powered marketing automation with cloud risk management, healthcare enterprises can achieve scalable, secure, and compliant digital engagement. This study contributes a structured methodology for designing, implementing, and governing secure AI-driven marketing ecosystems in healthcare organizations.
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1. Genne, S. (2022). A secure architecture for real-time data exchange in HIPAA-compliant patient portals. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 6202–6215.
2. Hasenkhan, F., Keezhadath, A. A., & Amarapalli, L. (2023). Intelligent Data Partitioning for Distributed Cloud Analytics. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 106-145.
3. Lokiny, N. (2022). Kubernetes for container orchestration in artificial intelligence cloud technologies. International Journal of Science and Research (IJSR), 11(11), 1536-1538.
4. Anumula, S. R. (2022). Governance frameworks for automated enterprise decision systems. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 137–157.
5. Ponugoti, M. (2023). Bridging the digital divide: Architecture for equitable technological access. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 6991–7002.
6. Madheswaran, M., Dhanalakshmi, R., Ramasubramanian, G., Aghalya, S., Raju, S., & Thirumaraiselvan, P. (2024, April). Advancements in immunization management for personalized vaccine scheduling with IoT and machine learning. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 1566-1570). IEEE.
7. Singh, A. (2021). Mitigating DDoS attacks in cloud networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(4), 3386–3392. https://doi.org/10.15662/IJEETR.2021.0304003
8. Gaddapuri, N. S. (2023). A COMPARATIVE STUDY OF HEALTHCARE SYSTEMS IN THE UNITED STATES AND INDIA. Power System Protection and Control, 51(2), 18-31.
9. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
10. Natta, P. K. (2023). Harmonizing enterprise architecture and automation: A systemic integration blueprint. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9746–9759. https://doi.org/10.15662/IJRPETM.2023.0606016
11. Kondisetty, K., Panda, M. R., & Murthy, C. J. (2023). Customer Experience Enhancement in Omnichannel Banking Using Reinforcement Learning. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 565-600.
12. Kesavan, E. (2023). Assessing laptop performance: A comprehensive evaluation and analysis. Recent Trends in Management and Commerce, 4(2), 175–185. https://doi.org/10.46632/rmc/4/2/22
13. Devi, C., Musunuru, M. V., & Mohammed, A. S. (2023). Reinforcement-Learning Scheduler for Multi-Tenant Spark Clustersunder Privacy Constraints. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 496-527.
14. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
15. 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.
16. Rajendran, S. (2023). Privacy preserving data mining using hiding maximum utility item first algorithm by means of grey wolf optimisation algorithm.
17. 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.
18. 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.
19. Chennamsetty, C. S. (2023). Neural Pipeline Orchestration: Deep Learning Approaches to Software Development Bottleneck Elimination. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(4), 8674-8680.
20. Surisetty, L. S. (2022). Designing Intelligent Integration Engines for Healthcare: From HL7 and X12 to FHIR and Beyond. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(1), 5989-5998.
21. Vimal Raja, G. (2024). Intelligent Data Transition in Automotive Manufacturing Systems Using Machine Learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
22. 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.
23. Kamadi, S. (2021). Risk Exception Management in Multi-Regulatory Environments: A Framework for Financial Services Utilizing Multi-Cloud Technologies.
24. Patnaik, S. K., Sidhu, M. S., Gehlot, Y., Sharma, B., & Muthu, P. (2018). Automated skin disease identification using deep learning algorithm. Biomedical & Pharmacology Journal, 11(3), 1429.
25. Mudunuri, P. R. (2022). Automating compliance in biomedical DevOps: A policy-as-code approach. International Journal of Research and Applied Innovations (IJRAI), 5(2), 6770–6783.
26. Konakalla, K. (2024). Building an end-to-end hiring process in Salesforce: Automating recruitment with custom objects, approval processes, and Lightning components. International Journal of Scientific Research in Engineering and Management, 8, 1-6.
27. Gopisetty, S. (2023). Helping Ephemeral Kubernetes Keep a Permanent, Honest Diary: An AI‑Powered Audit Companion for Fintech Models. European Journal of Advances in Engineering and Technology, 10(8), 93-121.
28. Polamreddy, V. R. (2022). Architecting Hybrid Synchronization Models to Enable Safe International Platform Transitions. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 6216-6229.
29. Manda, P. (2023). Migrating Oracle Databases to the Cloud: Best Practices for Performance, Uptime, and Risk Mitigation. International Journal of Humanities and Information Technology, 5(02), 1-7.
30. Makkena, B. (2023). PromptOps: Building prompt-driven DevOps workflows for infrastructure-as-code automation. International Journal of Communication Networks and Information Security, 15(10), 12–30.
31. Gollapudi, R. (2024). Event-aware multi-layer storage risk forecasting for Oracle database estates using HAPF. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.5183
32. Subramanyam, S. P. (2024). AI-driven CI/CD pipelines engineering for Kubernetes based cloud applications. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(1), 7514–7523.
33. Namdeo, A., Atulkar, A., & Porwal, R. K. (2022, August). Investigation of Two-Stage Epicyclic Gearbox for an Automobile for Energy Regeneration. In Biennial International Conference on Future Learning Aspects of Mechanical Engineering (pp. 363-376). Singapore: Springer Nature Singapore.
34. Panyala, V. R. (2022). Integrating AI-driven autoscaling mechanisms in Kubernetes-based microservices architectures. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(4), 9–21.
35. Boddupally, H. L. (2023). Automating Incident Triage and Root Cause Intelligence Through Large Language Model–Driven Correlation of System Logs and Operational Metrics in Large-Scale Distributed Environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7676-7688.
36. Sugumar, R. (2024). Quantum-Resilient Cryptographic Protocols for the Next-Generation Financial Cybersecurity Landscape. International Journal of Humanities and Information Technology, 6(02), 89-105.
37. Sriramoju, S. (2022). API-driven account onboarding framework with real-time compliance automation. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8132–8144.
38. Gopinathan, V. R. (2024). Meta-Learning–Driven Intrusion Detection for Zero-Day Attack Adaptation in Cloud-Native Networks. International Journal of Humanities and Information Technology, 6(01), 19-35.
39. Chivukula, V. (2023). Calibrating Marketing Mix Models (MMMs) with Incrementality Tests. International Journal of Research and Applied Innovations, 6(5), 9534-9538.
40. Gangina, P. (2022). Resilience engineering principles for distributed cloud-native applications under chaos. International Journal of Computer Technology and Electronics Communication, 5(5), 5760–5770.