Intelligent Enterprise Systems powered by AI and Cloud for Secure Adaptive and Resilient Transformation

Subrahmanyasarma Chitta; Subrahmanyasarma Chitta

doi:10.15662/IJSRAT.2024.0706008

Intelligent Enterprise Systems powered by AI and Cloud for Secure Adaptive and Resilient Transformation

Subrahmanyasarma Chitta

Abstract

The rapid advancement of digital technologies has transformed enterprise systems into highly dynamic and data-driven environments. Intelligent enterprise systems powered by artificial intelligence (AI) and cloud computing have emerged as a critical solution for achieving secure, adaptive, and resilient business transformation. This research explores how the integration of AI techniques, including machine learning and deep learning, with cloud infrastructure enables enterprises to enhance operational efficiency, improve decision-making, and strengthen cybersecurity. AI-driven models facilitate real-time data analysis, predictive insights, and automated responses, allowing systems to adapt to changing business conditions and evolving threats. Cloud computing provides scalable and flexible infrastructure that supports the deployment of intelligent applications across distributed environments. The combination of AI and cloud technologies enables the development of self-healing, autonomous systems capable of maintaining performance and security with minimal human intervention. This study proposes a framework for intelligent enterprise systems that emphasizes resilience, adaptability, and proactive risk management. The findings indicate that organizations adopting AI-powered cloud systems can achieve improved reliability, reduced operational costs, and enhanced protection against cyber threats. The research highlights the importance of integrating intelligent technologies to support sustainable and secure digital transformation in modern enterprises.

Article Information

Journal	International Journal of Science, Research and Technology
Volume (Issue)	Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT)
DOI	https://doi.org/10.15662/IJSRAT.2024.0706008
Pages	13210-13218
Published	December 25, 2024
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Subrahmanyasarma Chitta (%2024). Intelligent Enterprise Systems powered by AI and Cloud for Secure Adaptive and Resilient Transformation. International Journal of Science, Research and Technology , Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 13210-13218. https://doi.org/10.15662/IJSRAT.2024.0706008

References

1. Subramanyam, S. P. (2023). Secure identity and access management frameworks for cloud native DevOps systems. International Journal of Computer Technology and Electronics Communication, 6(4), 7357–7366.
2. Sharma, Ankit and Mulgund, Pavankumar and Srivastava, Adarsh and Agrawal, Lavlin, Beyond Cryptocurrency: There’s More to Blockchain (January 07, 2020). Beyond Cryptocurrency: There's More to Blockchain," Amplify, Cutter Consortium, January 7, 2020., Available at SSRN: https://ssrn.com/abstract=6098906 or http://dx.doi.org/10.2139/ssrn.6098906
3. Yamsani, N. (2024). Large Language Models for Intelligent Data Stewardship in Enterprises: Architectures, Provenance, and Evidence-Mapped Governance. International Journal of Computer Technology and Electronics Communication, 7(1), 8210-8219.
4. Katta, T. B. (2022). Cloud-native integration frameworks for modern enterprises: Driving scalable and resilient digital transformation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(3), 4926–4938.
5. Agarwal, S. (2022). Observability in Microservices: From Traditional Monitoring to Distributed System Intelligence. International Journal of Computer Technology and Electronics Communication, 5(6), 16220-16226.
6. Namdeo, A. (2023). Generative synthetic data pipelines for bias-free BI training. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 6(1), 10818–10826. https://doi.org/10.15662/IJAESIT.2023.0601003
7. Boddupally, H. L. (2022). Toward self-optimizing enterprise applications: AI-guided profiling and performance optimization for C# and SQL-based systems. SSRN. https://doi.org/10.2139/ssrn.6270498
8. Vootla A. (2024). AI-enhanced user interface refactoring for legacy healthcare portals. International Journal of Engineering & Extended Technologies Research, 6(5), 8835–8847.
9. Parepalli, S. (2020). Data-Centric Prediction of ETL Throughput and Resource Utilization Using Classical Machine Learning Models. Journal of Artificial Intelligence, Machine Learning and Data Science, 1, 3164-3174.
10. Hebbar, K. S. (2022). Machine learning-assisted service boundary detection for modularizing legacy systems. International Journal of Applied Engineering & Technology, 4(2), 401–414.
11. Mudunuri, P. R. (2022). Engineering audit-ready CI/CD pipelines for federally regulated scientific computing. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5342-5351.
12. Panyala, V. R. (2023). AI-augmented DevOps frameworks for accelerating cloud-native platform engineering at scale. International Journal of Research and Applied Innovations, 6(1), 8375–8379.
13. Vankayala, S. C. (2024). Quality intelligence: Leveraging quality analytics to drive business intelligence and customer experience. International Journal of Scientific Research in Science, Engineering and Technology. https://d1wqtxts1xzle7.cloudfront.net/126069916/qualityIntelligence14133-libre.pdf
14. Sheta, S. V. (2021). Security vulnerabilities in cloud environments. Webology, 18(6), 10043–10063.
15. Khan, M. F., Mubasher, M. M., Khan, W. A., Shabbir, G., & Saqib, S. (2024). Systematic Literature Review to Explore use of VR in Transportation Research to Study Driver Behavior. Journal of Computing and Artificial Intelligence, 2(2).
16. Kanthakhoo, N. (2023). Liquid Biopsy–Based Biomarkers for Early Detection of Breast and Colorectal Cancer. SRMS JOURNAL OF MEDICAL SCIENCE, 8(02), 152-160.
17. Chaturvedi V. (2023). Modern software development with Java, Spring Boot, and Python: A survey of frameworks and best practices. ESP Journal of Engineering & Technology Advancements, 3(4), 188–197.
18. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661
19. Sravanthi Mallireddy, D. R. S. (2024). Howzs Digital Transformation Impacted on HealthCare and Financial Services. Journal of Technological Innovations, 5(3).
20. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
21. Pasumarthi, H. (2023). A Deep Dive into Enterprise B2B Integrations: Designing High-Availability File and API Workflows with IBM Datapower and Autosys. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8363-8370.
22. Ghanta, S. (2021). A system-level approach to intelligent root cause discovery in distributed Java microservices. International Journal of Science, Engineering and Technology. https://doi.org/10.5281/zenodo.17760543
23. Thumala, S. R., & Pillai, B. S. (2024). Cloud Cost Optimization Methodologies for Cloud Migrations. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 4797-4809.
24. 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.
25. Ireddy, R. K. (2023). API-driven interoperability framework for corporate treasury management: A financial data exchange standard implementation with secure data aggregation networks. World Journal of Advanced Research and Reviews, 19(2), 1727-1738.
26. Meka, S. (2024). Securing Instant Payments: Implementing Fraud Prevention Frameworks with AVS and OTP Validation. Journal Code, 1763, 4821.
27. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
28. 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.
29. Sanepalli, Uttama Reddy. (2023). Cybersecurity Framework for Multi-Cloud Deployment Pipelines: A Zero-Trust Architecture for Inter-Platform Data Protection. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 6(1), 191-206.
30. Niture, N. A., & Abdellatif, I. (2020, October). Ai based airplane air pollution identification architecture using satellite imagery. In 2020 IEEE Cloud Summit (pp. 150-155). IEEE.
31. Sarabhu, V. B., & Balaji, V. (2018). Advanced memory virtualization technique for efficient access of data resources in cloud environment. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 1(3), 623–629.
32. Kasireddy, J. R. (2023). Operationalizing lakehouse table formats: A comparative study of Iceberg, Delta, and Hudi workloads. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8371-8381.
33. Viswanathan, V. (2023). Generative AI for smarter workforce planning and enterprise resource decisions. Journal of Information Systems Engineering and Management, 8(4), e-ISSN 2468-4376.
34. Gentyala, R. (2022). Beyond the Algorithm: A Longitudinal Analysis of Data Heterogeneity and Clinician Trust as Determinants of Predictive Tool Adoption and Patient Outcomes in Personalized Medicine. International Journal of AI, BigData, Computational and Management Studies, 3(2), 137-168.
35. Akila, R. (2024). A deep reinforcement learning approach for optimizing inventory management in the agri-food supply chain. J. Electrical Systems, 20(4s), 2238-2247.
36. Joyce, S. (2023). Optimizing SAP workloads on cloud-native platforms: A framework for intelligent resource allocation and performance scaling. International Journal of Science, Research and Technology (IJSRAT), 6(1), 9210–9219. https://doi.org/10.15662/IJSRAT.2023.0601002.
37. Prasad, P. K. (2019). DevSecOps: Securing infrastructure in the age of automation. International Journal of Research Publication in Engineering, Technology and Management, 2(1), 930–938.
38. Ganesan, M. (2024). Transforming home electronics customer self-installation experience with AI. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(4), 14319–14327.
39. Padala, S. (2024). Group-ID-Based Intelligent Routing: A Precision Routing Framework for Insurance Service Operations. International Journal of AI, BigData, Computational and Management Studies, 5(3), 183-187.