AI Driven Secure Enterprise Healthcare Marketing Automation using Machine Learning with Cloud Risk Management

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Published: 2024-06-21
DOI: https://doi.org/10.15662/IJRPETM.2024.0703012
Keywords:
Artificial Intelligence, Machine Learning, Healthcare Marketing Automation, Cloud Risk Management, Data Privacy, Cybersecurity, Predictive Analytics, Regulatory Compliance, Enterprise Systems, Secure Cloud Infrastructure

Main Article Content

T.Poovizhi
Assistant Professor, Dept. of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India

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.

Article Details

Issue

Vol. 7 No. 3 (2024): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

AI Driven Secure Enterprise Healthcare Marketing Automation using Machine Learning with Cloud Risk Management. (2024). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10546-10555. https://doi.org/10.15662/IJRPETM.2024.0703012

References

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. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.

7. 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.

8. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.

9. 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

10. 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.

11. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.

12. 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

13. 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.

14. 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

15. 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.

16. 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.

17. 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.

18. Rajendran, S. (2023). Privacy preserving data mining using hiding maximum utility item first algorithm by means of grey wolf optimisation algorithm.

19. 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.

20. 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.

21. 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.

22. 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.

23. 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.

24. 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.

25. 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.

26. Kamadi, S. (2021). Risk Exception Management in Multi-Regulatory Environments: A Framework for Financial Services Utilizing Multi-Cloud Technologies.

27. 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.

28. 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.

29. 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.

30. 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.

31. 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.

32. Chivukula, V. (2023). Calibrating Marketing Mix Models (MMMs) with Incrementality Tests. International Journal of Research and Applied Innovations, 6(5), 9534-9538.

33. 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.