Integrated Cloud-Native AI and ML Framework for Secure and Compliant Healthcare–Financial Systems
DOI:
https://doi.org/10.15662/IJRAI.2025.0805016Keywords:
Cloud-Native Platforms, AI, Machine Learning, Enterprise Systems, Healthcare Communication, Financial Systems, Security by Design, Data Privacy, Predictive Analytics, Scalable ArchitectureAbstract
The adoption of cloud-native AI and machine learning (ML) platforms is transforming enterprise systems across healthcare and financial sectors by enabling scalable, data-driven decision-making and secure, efficient communication. Cloud-native architectures allow applications to fully leverage cloud computing benefits, including elasticity, resilience, and service orchestration, while AI and ML provide predictive analytics, automation, and intelligent insights. In healthcare, AI-enabled platforms enhance patient communication, streamline clinical workflows, and support telemedicine services, while ensuring compliance with privacy regulations such as HIPAA. In financial systems, AI and ML facilitate fraud detection, risk management, and real-time transactional analysis. Security by design is critical for these platforms, integrating encryption, authentication, and access control into the architecture from inception to prevent data breaches, ensure compliance, and maintain trust among stakeholders. This research investigates the design, implementation, and evaluation of cloud-native AI and ML enterprise platforms tailored for healthcare communication and financial systems, emphasizing security, scalability, and performance. Simulation and case studies demonstrate that integrating cloud-native AI/ML with security by design improves operational efficiency, ensures secure data exchange, and enhances decision-making capabilities. The study provides guidelines for developing resilient, secure, and intelligent enterprise platforms.References
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