Articles Open Access
Intelligent Enterprise Platforms through Artificial Intelligence Hybrid Cloud Computing and Adaptive Cybersecurity
Software Architect, Globant, Mexico
Abstract
The rapid evolution of digital technologies has transformed enterprise operations, leading organizations to adopt intelligent platforms that integrate Artificial Intelligence (AI), Hybrid Cloud Computing, and Adaptive Cybersecurity. These technologies collectively provide organizations with enhanced computational capabilities, automated decision-making, secure data management, and scalable infrastructure to meet growing business demands. Artificial Intelligence enables enterprises to process vast amounts of structured and unstructured data, generating actionable insights that improve operational efficiency, customer experiences, and strategic planning. Hybrid cloud computing combines private and public cloud environments, offering flexibility, cost optimization, high availability, and seamless resource allocation while maintaining regulatory compliance and business continuity. Adaptive cybersecurity introduces dynamic and intelligent defense mechanisms capable of detecting, preventing, and responding to evolving cyber threats in real time using machine learning and behavioral analytics.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 7 |
| Number | 4 |
| Pages | 12838-12846 |
| Published | 2023-07-07 |
| Issue | Vol. 7 No. 4 (2023): International Research Journal of Innovative Engineering (IRJIE) |
References
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25. Devineni, A. (2022). Proactive incident detection in multi-tenant financial cloud platforms. International Journal of Science, Research and Technology, 5(4), 8136-8139.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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
7. Lanka, S. (2023). Built for the Future How Citrix Reinvented Security Monitoring with Analytics. International Journal of Humanities and Information Technology, 5(02), 26-33.
8. 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
9. 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
10. Juvvadi, R. R. (2018). Continuous accounting: Toward a real-time financial reporting architecture for the modern enterprise. Computer Fraud & Security, 2018(12), 33–41.
11. 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.
12. 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.
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. 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
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. Gummadi, V. P. K. (2020). API design and implementation: RAML and OpenAPI specification. Journal of Electrical Systems, 16(4).
21. 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.
22. 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/
23. 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.
24. 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.
25. Devineni, A. (2022). Proactive incident detection in multi-tenant financial cloud platforms. International Journal of Science, Research and Technology, 5(4), 8136-8139.
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