Articles Open Access
Predictive Enterprise Resilience using Advanced Digital Twin Technology and Cloud-Native Machine Learning
Independent Researcher, USA
Abstract
Enterprise resilience has become a critical organizational capability as businesses increasingly operate within interconnected digital, physical, and cloud-based environments exposed to cyber threats, supply-chain disruptions, infrastructure failures, market volatility, and operational uncertainty. Advanced digital twin technology combined with cloud-native machine learning offers a promising approach for predicting disruptions and supporting proactive resilience management. This study examines how digital twins can provide dynamic representations of enterprise assets, processes, dependencies, and operational states, while cloud-native machine-learning models analyze real-time and historical data to identify emerging risks and predict potential disruptions. The proposed approach integrates Internet of Things data, enterprise applications, cloud infrastructure, simulation models, and machine-learning pipelines into a continuously updated resilience framework. The methodology emphasizes data acquisition, digital-twin construction, predictive model development, scenario simulation, and resilience evaluation. Machine-learning techniques can estimate disruption probabilities, detect anomalies, forecast resource requirements, and evaluate alternative recovery strategies. Cloud-native architecture enables scalable model training, distributed processing, automated deployment, and continuous learning. The study proposes that integrating these technologies can improve early-warning capabilities, decision support, adaptive resource allocation, and recovery planning. The resulting framework provides a foundation for enterprises seeking to move from reactive continuity management toward predictive and continuously adaptive resilience
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 10 |
| Number | 3 |
| Pages | 18891-18899 |
| Published | 2026-06-15 |
| Issue | Vol. 10 No. 3 (2026): International Research Journal of Innovative Engineering (IRJIE) |
References
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30. Ramasamy, M. (2026). Microservices-based architectures for enterprise network orchestration and assurance. International Journal of Research and Applied Innovations, 9(2), 156–166.
31. Zhou, G., Tian, W., Buyya, R., Xue, R., & Song, L. (2024). Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions. Artificial Intelligence Review, 57, 124. https://doi.org/10.1007/s10462-024-10756-9
2. Ramasamy, M. (2025). Secure and extensible platform architecture for enterprise network orchestration. International Journal of Science, Research and Technology, 8(1), 13534–13544.
3. Mudunuri, L. N. R., Maroju, P. K., & Aragani, V. M. (2025, January). Leveraging nlp-driven sentiment analysis for enhancing decision-making in supply chain management. In 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-6). IEEE.
4. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
5. Mohile, A., Yadav, A. L., Mukherjee, U., Kapoor, R., Attri, V., & Reddy, R. R. (2026, May). Ethical AI Framework for Protecting Human Rights in Digital Surveillance Systems. In 2026 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE) (pp. 1-6). IEEE.
6. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
7. Chinnam, N. B. (2025). Intelligent distribution center integration for transportation modernization. International Journal of Research and Applied Innovations, 8(2), 11283–11288.
8. Pasupuleti, N. S., Kapoor, S., Vedula, J., Sati, M. M., Shamilevna, G. S., & Khurmat, E. (2025, July). Implementation of a Deep Learning Model for Real-time Detection of Diabetic Retinopathy in Primary Care Clinics. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-6). IEEE.
9. Awopejo, T. E., Adigun, P. O., Oyekanmi, T. T., Azeez, N. A. A., Adekanye, M. A., & Obisesan, A. (2025). Machine learning-based prediction of magnetic properties from hysteresis curves: A comparative study of Random Forest, Gradient Boosting, XGBoost, LightGBM and Support Vector Regressions. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(6), 13456-13479.
10. 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.
11. Tarakampet, S., Tatavarthi, S., & Ali, S. B. S. (2026, May). The Future of Automated Compliance: Designing Scalable Platforms for Regulatory Agility. In 2026 IEEE World AI IoT Congress (AIIoT) (pp. 0974-0980). IEEE.
12. Bitragunta, S. L. V. (2024). AI-driven deep learning-based animal intrusion detection and early warning system for railroad tracks. International Research Journal of Innovative Engineering, 8(1), 13909–13918.
13. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
14. Gopinathan, V. R. (2023). Modernizing Enterprise Applications Using Intelligent API Frameworks Machine Learning and Cloud Native Computing. International Journal of Science, Research and Technology, 6(5), 10690-10697.
15. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3-15.
16. Lingala, B. (2026). Artificial intelligence and society: Governance models and ethical risks. International Journal of Research and Applied Innovations, 9(1), 13707–13711.
17. Das, P., Gogineni, A., Patel, K., & Jain, A. (2025, May). Integration of IoT and Machine Learning to Monitor the Air Quality by Measuring the Block Carbon Levels. In 2025 International Conference on Engineering, Technology & Management (ICETM) (pp. 1-6). IEEE.
18. Raja, G. V. (2022). AI Enabled Cloud and Data Engineering Frameworks for Secure, Scalable, and Intelligent Cyber Physical Analytics Systems. International Journal of Research and Applied Innovations, 5(4), 7395-7409.
19. Ahuja, D. (2025). Securing Container Isolation in Multi-Tenant Environments. Journal of Computer Science and Technology Studies, 7(10), 225-232.
20. Rekulapalli, K., Kagga, S. R., Ayyagari, V., Akula, A., & Raj Akula, R. (2026). AI in HRM: Enhancing workforce competency and organizational effectiveness. Proceedings of the International Conference on Innovative Computing and Communication (ICICC 2026).
21. Elliott, S., & Roy, S. (2026, April). Critical Transit Infrastructure in Smart Cities and Urban Air Quality: A Multi-City Seasonal Comparison of Ridership and PM 2.5. In 2026 IEEE Conference on Technologies for Sustainability (SusTech) (pp. 1-8). IEEE.
22. Pothuri, M. K. (2025). AI-Driven Reusable Unified Extract for Multi-State Medicaid and Federal Reporting-a Product that saves Millions of Taxpayer Money through process efficiency and reusability. International Journal of AI, BigData, Computational and Management Studies, 6(4), 211-216.
23. Polamarasetty, V. K. (2021). Modernizing SAP sales and distribution systems through ABAP-based enterprise solutions. International Journal of Research and Applied Innovations, 4(2), 4925–4930.
24. Manda, P. (2026). Database modernization - Sybase to SQL Server migration. International Journal of Engineering & Extended Technologies Research, 8(1), 252–258.
25. Sugumar, R. (2022). Federated Learning and Distributed AI Architectures for Cloud-Based Cyber Healthcare Data Collaboration Systems. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9232.
26. Chundi, V. R. K., Agarwal, V., & Arya, P. (2025, November). Green AI for Sustainable Supply Chains: Challenges in Emerging Economies. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1-5). IEEE.
27. Soundappan, S. J. (2021). Integrated Artificial Intelligence Framework for Enterprise Cloud Modernization and Intelligent Threat Management Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(4), 5274-5284.
28. Shaik, N. (2025). Securing Kubernetes: An integrated approach to AI-driven threat detection and eBPF-based security monitoring. International Journal of Research and Applied Innovations, 8(2), 11289–11294
29. Mathew, A., & Sydney, W. (2026). Cybersecurity 5.0: From firewalls to fully autonomous digital protection. ISRG Journal of Engineering and Technology, 2(2), 6–9.
30. Ramasamy, M. (2026). Microservices-based architectures for enterprise network orchestration and assurance. International Journal of Research and Applied Innovations, 9(2), 156–166.
31. Zhou, G., Tian, W., Buyya, R., Xue, R., & Song, L. (2024). Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions. Artificial Intelligence Review, 57, 124. https://doi.org/10.1007/s10462-024-10756-9
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