AI-Driven Cloud-Native Enterprise Systems for Secure Financial, Healthcare, and Intelligent Automation Platforms
Main Article Content
Abstract
Artificial Intelligence (AI) combined with cloud-native enterprise architectures is transforming modern digital ecosystems across financial services, healthcare, and intelligent automation domains. Organizations are increasingly adopting microservices, containerization, DevSecOps, and serverless computing to create scalable, resilient, and secure systems capable of handling high-volume transactions, sensitive data, and real-time analytics. This paper presents a comprehensive framework for designing AI-driven cloud-native enterprise systems that prioritize security, compliance, performance optimization, and intelligent decision-making.
In financial services, AI-enabled cloud systems support fraud detection, algorithmic trading, risk assessment, and personalized customer experiences. In healthcare, these platforms facilitate predictive diagnostics, medical imaging analysis, patient data management, and remote care monitoring while ensuring strict adherence to regulatory standards such as HIPAA and GDPR. Intelligent automation platforms integrate robotic process automation (RPA), machine learning (ML), and natural language processing (NLP) to streamline enterprise workflows, reduce operational costs, and enhance decision intelligence.
The proposed architecture leverages container orchestration (e.g., Kubernetes), API-first design, zero-trust security models, data encryption strategies, and multi-cloud hybrid deployments to ensure system reliability and resilience. AI lifecycle management (MLOps), observability frameworks, and automated governance mechanisms are incorporated to address model drift, data bias, and operational risks. The research emphasizes secure data pipelines, role-based access control (RBAC), distributed identity management, and blockchain-inspired audit mechanisms for transparent compliance tracking.
This study synthesizes recent advancements in AI infrastructure, edge computing, federated learning, and cloud security to propose a scalable enterprise reference architecture. The methodology combines architectural modeling, comparative technology analysis, case study evaluation, and security risk assessment to validate the proposed framework. Results demonstrate that AI-driven cloud-native systems significantly improve scalability, reduce downtime, enhance cybersecurity posture, and enable intelligent automation across sectors.
The paper concludes by discussing challenges such as ethical AI governance, interoperability constraints, vendor lock-in risks, and quantum-resistant cryptography preparedness. Future research directions include autonomous cloud optimization, privacy-preserving AI techniques, and self-healing enterprise architectures. Overall, AI-driven cloud-native enterprise systems represent a transformative paradigm for secure, compliant, and intelligent digital infrastructures.
Article Details
Section
How to Cite
References
1. Gaddapuri, N. S. (2024). AI BASED CLOUD COMPUTATION METHOD AND PROCESS DEVELOPMENT. Power System Protection and Control, 52(2), 38-50.
2. Ramidi, M. (2024). Cross-platform performance optimization strategies for large-scale mobile applications. International Journal of Humanities and Information Technology (IJHIT), 6(1), 44–63.
3. 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.
4. Ponnoju, S. C., & Paul, D. (2023). Hybridizing Apache Camel and Spring Boot for Next-Generation microservices in financial data integration. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 209-244.
5. Anumula, S. R. (2024). Cross-domain learning frameworks for enterprise decision systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(3), 14059–14068.
6. Harish, M., & Selvaraj, S. K. (2023, August). Designing efficient streaming-data processing for intrusion avoidance and detection engines using entity selection and entity attribute approach. In AIP Conference Proceedings (Vol. 2790, No. 1, p. 020021). AIP Publishing LLC.
7. Karthikeyan, K., Umasankar, P., Uthirasamy, R., Parathraju, P., & Thiyagarajan, J. (2024). Design and Implementation of Dual Solar Tracking System for Street Lights. J. Electrical Systems, 20(2), 207-216.
8. Selvi, C. P., Muneeshwari, P., Selvasheela, K., & Prasanna, D. (2023). Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER. Intelligent Automation & Soft Computing, 35(3).
9. Mudunuri, P. R. (2024). Scalable secrets governance models for high-sensitivity biomedical systems. International Journal of Computer Technology and Electronics Communication (IJCTEC), 7(1), 8220–8232.
10. Dhanya, P. M., & Ananth, S. (2013). Efficient Traffic Congestion Detection Method in Vanet. International Journal for Technological Research in Engineering, 1(3).
11. Surampudi, Y., Kondaveeti, D., & Pichaimani, T. (2023). A Comparative Study of Time Complexity in Big Data Engineering: Evaluating Efficiency of Sorting and Searching Algorithms in Large-Scale Data Systems. Journal of Science & Technology, 4(4), 127-165.
12. Kubam, C. S., Duggirala, J., VishnubhaiSheta, S., Mogali, S. K., Lakhina, U., & Kaur, H. (2025, November). AI-Driven Credit Risk Assessment in Digital Finance Using Feature Optimization Deep Q Learning. In 2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 210-216). IEEE.
13. Nagarajan, C., Neelakrishnan, G., Akila, P., Fathima, U., & Sneha, S. (2022). Performance Analysis and Implementation of 89C51 Controller Based Solar Tracking System with Boost Converter. Journal of VLSI Design Tools & Technology, 12(2), 34-41p.
14. Ponugoti, M. (2024). Engineering global resilience: A cloud-native approach to enterprise system. International Journal of Future Innovative Science and Technology (IJFIST), 7(2), 12392–12403.
15. Genne, S. (2023). Improving Enterprise Web Responsiveness through Server-Side Rendering in Next. js. International Journal of Computer Technology and Electronics Communication, 6(4), 7313-7323.
16. 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.
17. 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.
18. Muthusamy, P., Mohammed, A. S., & Ramalingam, S. (2021). Cloud-Native Customer Data Platforms (CDP): Optimizing Personalization Across Brands. American Journal of Autonomous Systems and Robotics Engineering, 1, 200-233.
19. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.
20. Kamadi, S. Multi-Cloud ETL Automation and Rollback Strategies: An Empirical Study for Distributed workload orchestration system. https://www.researchgate.net/profile/Sandeep-Kamadi/publication/399059730_Multi-Cloud_ETL_Automation_and_Rollback_Strategies_An_Empirical_Study_for_Distributed_workload_orchestration_system/links/694ca68106a9ab54f84a6805/Multi-Cloud-ETL-Automation-and-Rollback-Strate gies-An-Empirical-Study-for-Distributed-workload-orchestration-system.pdf
21. Rengarajan, A., & Rajagopalan, S. (2021). Chaos Blend LFSR-Duo Approach on FPGA for Medical Image Security. Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 3, 3, 155.
22. 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.
23. Ponnoju, S. C., Muthusamy, P., & Devi, C. (2022). Differentially Private Streaming Metrics with Laplace Noise in Apache Flink. American Journal of Autonomous Systems and Robotics Engineering, 2, 417-451.
24. Anitha, K., Vijayakumar, R., Jeslin, J. G., Elangovan, K., Jagadeeswaran, M., & Srinivasan, C. (2024, March). Marine Propulsion Health Monitoring: Integrating Neural Networks and IoT Sensor Fusion in Predictive Maintenance. In 2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) (pp. 1-6). IEEE.
25. 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.