AI Powered Cyber Resilient Cloud Architecture for Enterprise Systems Financial Platforms Healthcare Analytics and Intelligent Automation
DOI:
https://doi.org/10.15680/IJCTECE.2026.0902013Keywords:
AI-powered cybersecurity, cyber resilience, cloud computing, enterprise cloud architecture, financial platform security, healthcare analytics security, intelligent automation, machine learning threat detection, zero trust security, cloud security architecture, automated incident response, predictive threat analyticsAbstract
The rapid adoption of cloud computing, artificial intelligence (AI), and digital transformation technologies has significantly reshaped modern enterprise infrastructures. Organizations across sectors such as finance, healthcare, manufacturing, and digital services increasingly depend on cloud platforms to deliver scalable applications, large-scale data processing, and intelligent automation capabilities. However, this technological advancement has simultaneously expanded the cyber threat landscape, exposing organizations to increasingly sophisticated attacks including ransomware, distributed denial-of-service attacks, data breaches, insider threats, and advanced persistent threats. Traditional cybersecurity approaches based on static defenses and reactive monitoring are insufficient to protect complex cloud ecosystems. As a result, the concept of cyber resilience has emerged as a critical strategy to ensure systems can withstand, respond to, and recover from cyber incidents while maintaining operational continuity.
This paper proposes an AI-powered cyber resilient cloud architecture designed to enhance security, reliability, and adaptive defense mechanisms for enterprise systems, financial platforms, healthcare analytics infrastructures, and intelligent automation environments.
The architecture integrates artificial intelligence-based threat detection, zero-trust security principles, automated incident response, and self-healing infrastructure mechanisms to strengthen resilience against evolving cyber threats. By leveraging AI-driven analytics, behavioral monitoring, and predictive security intelligence, the proposed framework improves real-time threat detection and automated mitigation capabilities. The architecture also supports secure data management, regulatory compliance, and operational efficiency across sensitive domains such as financial services and healthcare. The results demonstrate that integrating AI technologies with cyber resilience principles can significantly enhance security posture, reduce downtime, and enable intelligent autonomous protection within modern cloud infrastructures.
References
1. Kubam, C. S., Duggirala, J., VishnubhaiSheta, S., Mogali, S. K., Lakhina, U., & Kaur, H., 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, Nov. 2025.
2. Seth, D. K., Ratra, K. K., & Sundareswaran, A. P., AI driven hybrid edge cloud architecture for real time big data analytics and scalable communication in retail supply chains, in Proc. IEEE SoutheastCon 2025, IEEE, 2025. (Indexed conference paper)
3. Jayaraman, S., Rajendran, S., & P, S. P., Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud, International Journal of Business Intelligence and Data Mining, vol. 15, no. 3, pp. 273-287, 2019.
4. Vimal Raja, G., Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration, International Journal of Multidisciplinary Research in Science, Engineering and Technology, vol. 5, no. 8, pp. 1336-1339, 2022.
5. Suddala, V. R. A. K., FADL-DP and CNN-GRU Driven Cloud Framework for Secure Healthcare E-Commerce Platform, in 2025 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), pp. 991-996, IEEE, Nov. 2025.
6. Soundappan, S. J., AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization, International Journal of Advanced Engineering Science and Information Technology (IJAESIT), vol. 7, no. 5, pp. 14905, 2024.
7. Kumar, R., Mohammed, A. S., & Murthy, C. J., Cash Management Forecasting Using Long Short-Term Memory (LSTM) Networks, American Journal of Cognitive Computing and AI Systems, vol. 7, pp. 123-155, 2023.
8. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E., Cloud-Based Extreme Learning Machines for Mining Waste Detoxification Efficiency, in 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 1348-1353, IEEE, Sept. 2025.
9. Ambati, K. C., An event-driven architecture for autonomous supply chain risk detection and decision automation, International Journal of Computer Technology and Electronics Communication (IJCTEC), vol. 8, no. 1, pp. 1202–1211, 2025.
10. Rajasekaran, M., Sekar, S., Manikandaprabhu, K., Vijayakumar, R., Rajmohan, M., & Murugan, S., Next-Gen Coaching: IoT and Linear Regression for Adaptive Training Load Management, in 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pp. 224-229, IEEE, Oct. 2024.
11. Thirumal, L., & Umasankar, P., Precision muscle segmentation and classification for knee osteoarthritis with dual attention networks and GAO-optimized CNN, Biomedical Signal Processing and Control, vol. 111, 108244, 2026.
12. Ande, B. R., Leveraging Azure OpenAI and Cognitive Services for Enterprise Automation: Streamlining Operations and Enhancing Decision-Making, J. Inf. Syst. Eng. Manag, vol. 9, no. 4s, pp. 209-216, 2024.
13. Seth, D. K., Ratra, K. K., & Sundareswaran, A. P., AI and generative AI driven automation for multi cloud and hybrid cloud architectures enhancing security performance and operational efficiency, in Proc. IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC), pp. 784–793, IEEE, 2025. https://doi.org/10.1109/CCWC62904.2025.10903928
14. Soundappan, S. J., AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization, International Journal of Advanced Engineering Science and Information Technology (IJAESIT), vol. 7, no. 5, pp. 14905, 2024.
15. Kumar, S. A., & Anand, L., A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms, KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, vol. 19, no. 11, pp. 3841-3855, 2025.
16. Gopinathan, V. R., Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking, International Journal of Computer Technology and Electronics Communication, vol. 7, no. 6, pp. 9837-9845, 2024.
17. Rajasekaran, M., Sekar, S., Manikandaprabhu, K., Vijayakumar, R., Rajmohan, M., & Murugan, S., Next-Gen Coaching: IoT and Linear Regression for Adaptive Training Load Management, in 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pp. 224-229, IEEE, Oct. 2024.
18. Kiran, A., Rubini, P., & Kumar, S. S., Comprehensive review of privacy, utility and fairness offered by synthetic data, IEEE Access, 2025.
19. Vimal Raja, G., Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration, International Journal of Multidisciplinary Research in Science, Engineering and Technology, vol. 5, no. 8, pp. 1336-1339, 2022.
20. Suddala, V. R. A. K., FADL-DP and CNN-GRU Driven Cloud Framework for Secure Healthcare E-Commerce Platform, in 2025 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), pp. 991-996, IEEE, Nov. 2025.
21. Jayaraman, S., Rajendran, S., & P, S. P., Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud, International Journal of Business Intelligence and Data Mining, vol. 15, no. 3, pp. 273-287, 2019.
22. Kumar, R., Mohammed, A. S., & Murthy, C. J., Cash Management Forecasting Using Long Short-Term Memory (LSTM) Networks, American Journal of Cognitive Computing and AI Systems, vol. 7, pp. 123-155, 2023.
23. Karnam, A., Rolling Upgrades, Zero Downtime: Modernizing SAP Infrastructure with Intelligent Automation, International Journal of Engineering & Extended Technologies Research, vol. 7, no. 6, pp. 11036–11045, 2025. https://doi.org/10.15662/IJEETR.2025.0706022
24. Thumala, S. R., Mane, V., Patil, T., Tambe, P., & Inamdar, C., Full Stack Video Conferencing App using TypeScript and NextJS, in 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), pp. 1285-1291, IEEE, June 2025.
25. Ambati, K. C., An event-driven architecture for autonomous supply chain risk detection and decision automation, International Journal of Computer Technology and Electronics Communication (IJCTEC), vol. 8, no. 1, pp. 1202–1211, 2025.
26. Panda, S. S., Delivering Scalable Cloud Services in China: Microsoft and 21Vianet Collaboration, International Journal of Advanced Research in Computer Science & Technology (IJARCST), vol. 7, no. 6, pp. 11325-11333, 2024.
27. Ratra, K. K., Seth, D. K., & Uppuluri, S., Energy efficient microservices architecture for large scale e commerce platforms, in Proc. 2025 IEEE Conference on Technologies for Sustainability (SusTech), IEEE, 2025. (Conference paper listing via publication record)
28. Konda, S. K., Sustainable energy optimization through cloud-native building automation and predictive analytics integration, World Journal of Advanced Research and Reviews, vol. 24, no. 3, pp. 3619–3628, 2024. https://doi.org/10.30574/wjarr.2024.24.3.3803
29. Anumula, S. R., Intelligent Microservices in Regulated Industries: Crew Scheduling and Retail Claims, Journal of Computer Science and Technology Studies, vol. 7, no. 6, pp. 1084-1089, 2025.
30. Kumar, S. A., & Anand, L., A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms, KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, vol. 19, no. 11, pp. 3841-3855, 2025.
31. Seth, D. K., Ratra, K. K., & Sundareswaran, A. P., AI driven hybrid edge cloud architecture for real time big data analytics and scalable communication in retail supply chains, in Proc. IEEE SoutheastCon 2025, IEEE, 2025. (Indexed conference paper)
32. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E., Cloud-Based Extreme Learning Machines for Mining Waste Detoxification Efficiency, in 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 1348-1353, IEEE, Sept. 2025.
33. Gopinathan, V. R., Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking, International Journal of Computer Technology and Electronics Communication, vol. 7, no. 6, pp. 9837-9845, 2024.
34. Thirumal, L., & Umasankar, P., Precision muscle segmentation and classification for knee osteoarthritis with dual attention networks and GAO-optimized CNN, Biomedical Signal Processing and Control, vol. 111, 108244, 2026.
35. Soundappan, S. J., AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization, International Journal of Advanced Engineering Science and Information Technology (IJAESIT), vol. 7, no. 5, pp. 14905, 2024.
