Next-Generation Enterprise Engineering through Artificial Intelligence Integrated Cloud Automation and Predictive Cyber Resilience
Abstract
The rapid evolution of digital technologies has transformed enterprise engineering into a strategic discipline that integrates intelligent automation, cloud computing, and cyber resilience to improve organizational performance and sustainability. Artificial Intelligence (AI) has emerged as a fundamental technology for enabling intelligent decision-making, predictive analytics, process optimization, and autonomous system management. Simultaneously, cloud automation has revolutionized enterprise infrastructure by providing scalable, flexible, and cost-effective computing environments that support dynamic business operations. Predictive cyber resilience complements these technologies by employing machine learning algorithms, behavioral analytics, and real-time threat intelligence to anticipate, detect, and mitigate cyber threats before they disrupt critical business services. This integrated approach enhances operational efficiency, strengthens cybersecurity, minimizes downtime, and supports continuous innovation across enterprise ecosystems. Organizations adopting AI-integrated cloud automation with predictive cyber resilience benefit from automated workflows, intelligent resource allocation, proactive risk management, and improved compliance with evolving regulatory requirements. Furthermore, these technologies facilitate digital transformation by enabling data-driven decision-making, adaptive infrastructure management, and resilient enterprise architectures capable of responding to rapidly changing market demands. This study explores the convergence of Artificial Intelligence, cloud automation, and predictive cyber resilience within next-generation enterprise engineering. It examines their technological foundations, implementation strategies, benefits, and challenges while proposing an integrated framework that supports secure, intelligent, and sustainable enterprise operations in the era of Industry 5.0 and digital transformation.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 8 No. 5 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
14343-14362 |
Published |
October 10, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Ajay Chakravarty (%2023). Next-Generation Enterprise Engineering through Artificial Intelligence Integrated Cloud Automation and Predictive Cyber Resilience. International Journal of Emerging Trends in Engineering and Management Research , Vol. 8 No. 5 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 14343-14362. https://doi.org/10.15662/ijetemr.2023.0805004 |
References
2. 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.
3. Parasa, M. (2022). Addressing the underutilization of exit interview data: A structured AI-assisted framework for actionable workforce insights in SAP SuccessFactors. Global Scientific and Academic Research Journal of Multidisciplinary Studies, 1(6), 42–52. https://gsarpublishers.com/abstract-2326/
4. Raja, G. V. (2022). Integrating network forensics with data mining for advanced cybercrime investigation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5321-5326.
5. Mohammed, S., & Polamarasetty, V. K. (2021). Enterprise multi-cloud transformation and managed services modernization. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 4(9), 2041–2056. https://doi.org/10.15680/IJMRSET.2021.0409023
6. Gollapudi R. Backup integrity and recovery readiness assessment for high-availability databases. Computer Fraud and Security. 2024;23.
7. Prasanna Kumar Natta. (2022). Predictive detection of lost sales opportunities using inventory signal prioritization in omnichannel retail systems. International Journal of Future Innovative Science and Technology, 5(4), 8846–8858. https://doi.org/10.15662/IJFIST.2022.0504003
8. Kotla, M. R. T. (2023). Autonomous enterprise integration: The future of self-healing data and API ecosystems. International Journal of Research and Applied Innovations (IJRAI), 6(3), 5968–5971.
9. Juvvadi, R. R. (2018). Continuous accounting: Toward a real-time financial reporting architecture for the modern enterprise. Computer Fraud & Security, 2018(12), 33–41.
10. Alex Roney Mathew. (2019). Malware analysis of API calls using FPGA hardware level security. International Journal for Research in Applied Science & Engineering Technology, 7(3), 898–900.
11. Veershetty, G. (2022). Digital modernization of gas utility operations: Architecture, scaled-agile delivery, and assurance. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7796.
12. Anbazhagan, K., & Sugumar, R. (2016). A proficient two level security contrivances for storing data in cloud. Indian Journal of Science and Technology, 9(48), 1–5. https://doi.org/10.17485/ijst/2016/v9i48/103399
13. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
14. 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.
15. Navandar, P. (2022). Adaptive SAP security control framework for ML driven anomaly detection, role based access hardening, and continuous compliance monitoring in SAP S/4HANA environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(3), 4939–4952. https://doi.org/10.15662/IJEETR.2022.0403005
16. Meesala, A. (2022). Adaptive Spread Anomaly Intelligence Framework (ASAIF): A cloud-native AI framework for real-time bid-ask spread anomaly detection and cross-venue liquidity risk intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9597–9604.
17. Joyce, S. (2022). Redefining Resilience Through Architectural Innovation and Operational Excellence in SAP HANA Backup Implementation on Microsoft Azure for Scalable Secure and Intelligent Data Protection. Journal Code, 1347, 8520.
18. Chaganti, S. (2022, November). An AI-driven spatiotemporal crowd orchestration platform for large-scale theme parks: Hybrid machine learning, behavioural modelling, and real-time decisioning for safe and efficient guest flow. International Journal on Recent and Innovation Trends in Computing and Communication, 10(11), 275–283.
19. Gummadi, V. P. K. (2020). API design and implementation: RAML and OpenAPI specification. Journal of Electrical Systems, 16(4).
20. Garg, A. (2022). Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States. In Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 (pp. 113-158). Academic Press.
21. Vimal Raja, G. (2022). 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, 5(8), 1336-1339.
22. Gopisetty, S. (2022). " Hey Jenkins, build my banking app": An LLM-Powered Assistant That Turns Plain English into Compliant CI/CD Pipelines for Non-Expert Developers. European Journal of Advances in Engineering and Technology, 9(11), 178-197.
23. Polamreddy, V. R. (2022). Architecting Hybrid Synchronization Models to Enable Safe International Platform Transitions. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 6216-6229.
24. Devineni, A. (2022). Proactive incident detection in multi-tenant financial cloud platforms. International Journal of Science, Research and Technology, 5(4), 8136-8139.
25. Manda, P. (2023). Leveraging AI to Improve Performance Tuning in Post-Migration Oracle Cloud Environments. International Journal of Research Publications in Engineering Technology and Management (IJRPETM), 6(3), 8714–8725.
26. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.