Integrating Artificial Intelligence with Cloud Computing for Enterprise Cybersecurity Data Intelligence and Intelligent Automation
DOI:
https://doi.org/10.15662/IJEETR.2026.0803015Keywords:
Artificial Intelligence, Cloud Computing, Enterprise Cybersecurity, Data Intelligence, Intelligent Automation, Machine Learning, Predictive Analytics, Cloud Security, Digital Transformation, Enterprise InfrastructureAbstract
The rapid evolution of digital technologies has transformed enterprise operations, creating increasing demands for secure, scalable, and intelligent computing environments. Integrating Artificial Intelligence (AI) with Cloud Computing provides a powerful framework for strengthening enterprise cybersecurity, enhancing data intelligence, and enabling intelligent automation. AI-driven cloud platforms utilize machine learning, deep learning, predictive analytics, and automated decision-making techniques to identify cyber threats, optimize data processing, and improve business operations. Cloud computing offers flexible infrastructure, large-scale data storage, and distributed computational capabilities that support the deployment of advanced AI applications across enterprise environments. This integration enables organizations to detect security anomalies, automate incident responses, extract valuable insights from complex datasets, and improve operational efficiency. The combination of AI and cloud technologies also addresses challenges related to scalability, real-time monitoring, privacy protection, and adaptive security management. This paper explores the role of AI-powered cloud computing in developing intelligent enterprise ecosystems, focusing on cybersecurity enhancement, data-driven decision-making, and automation of business processes. The study highlights emerging opportunities, implementation challenges, and future directions for building secure, intelligent, and resilient enterprise infrastructures capable of adapting to rapidly changing digital environments
References
1. Ali, M., Rahman, M. W., Hossain, M. S., & Hossain, M. S. (2026). Developing AI-Driven Business Intelligence Systems to Strengthen Economic Competitiveness AND Policy Innovation in the United States. Repository Antis Publisher, 692631.
2. Sivakumer, D. (2026). AI capability maturity assessment model for ServiceNow enabled digital enterprise transformation. International Journal of Science, Research and Technology (IJSRAT), 9(1), 100–110.
3. Meesala, A. (2024). Distributed securities pricing reconciliation at global scale: Price validation engine for financial institutions. World Journal of Advanced Research and Reviews, 21(2), 2212-2220.
4. Sundareswaran, A. P., Gupta, A., Srinivas, S., Athamakuri, S. S. K. K., Singh, K., & Sharma, R. K. (2025, August). Data Quality Assurance in Cloud-Based Warehousing Systems. In 2025 International Conference on Intelligent and Secure Engineering Solutions (CISES) (pp. 939-944). IEEE.
5. Prakashkumar, P. K. R. (2025). Secure bank integration framework for Oracle ERP Fusion: Enhancing payment disbursement, Auto Lockbox, and bank account reconciliation. European Economic Letters, 15(4), 2505–2517. https://doi.org/10.52783/eel.v15i4.4082
6. Islam, N. M., & Gomes, A. (2026). *Optimizing Medicaid program integrity: A data governance framework for detecting collusive fraud in New York's LHCSA and Social Adult Day Care sectors*. *American Journal of Economics and Business Management, 9*(3), 374–401. [https://doi.org/10.31150/ajebm.v9i3.4701](https://doi.org/10.31150/ajebm.v9i3.4701) ([ResearchGate][1])
7. Rao, G. R. (2023). Index lifecycle and shard allocation optimization in large-scale Elasticsearch clusters: A performance–cost trade-off analysis. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 6903–6907.
8. Bandaru, N. (2025). Architecting Compliance Ready Artificial Intelligence for Regulated Digital Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(4), 12463-12471.
9. Meesala, L. K. (2024). Agentic AI in cybersecurity: Dual-use dynamics, threat vectors, and governance imperatives. World Journal of Advanced Research and Reviews, 24(3), 3667-3672.
10. Gujarathi, M. (2025). Event-driven architecture in long-running enterprise validation workflows. International Journal of Research Publications in Engineering, Technology and Management, 8(4), 12572–12582.
11. Anumula, S. K., Bhwsar, R., & Patil, M. (2026). The digital backbone for architecting smart operations with a unified namespace. Discover Computing, 29(1), 300.
12. Chukkala, R. (2025, April). The Convergence of CCAI, Chatbots, and RCS Messaging: Redefining Business Communication in the AI Era. In International Conference of Global Innovations and Solutions (pp. 194-213). Cham: Springer Nature Switzerland.
13. Vasa, M. (2025). Scalable and Secure Infrastructure-as-Code Governance through Multi-Tenant Terraform Cloud Architectures. International Journal of Scientific Research in Science and Technology, 12(6), 661-670.
14. Mohammed, S. (2024). Strategic cloud cost optimization and FinOps governance for global enterprises. International Journal of Research and Applied Innovations (IJRAI), 7(6), 12004–12008.
15. Rohit Wadhwa. (2024). Security and Data Integrity Challenges in Event-Driven MicroservicesBased Distributed Enterprise Systems. International Journal of Computer Science and Information Technology Research, 5(3), 59–73.
16. Pothuri, M. K. (2025). Next-Gen Business Intelligence in Financial Services-Transforming Financial Efficiency with AI-Driven BI, Integration of AI/ML with BI tools. IJSAT-International Journal on Science and Technology, 16(4).
17. Patel, M., & Korat, U. (2026, March). Enhancing Indoor Localization Accuracy with Bluetooth Low Energy RSSI Signals Analysis Using Machine Learning Algorithms. In 2026 14th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-6). IEEE. Gurram, S. K. (2024). Federated learning for anomaly detection in distributed systems. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 14031–14040.
18. Mudusu, S. K. (2025, December 22). Cognitive data architecture: Designing self-optimizing frameworks for scalable AI systems. CIO (Foundry Expert Contributor Network).
19. Adepu, G. (2023). Large Language Model–Powered Public Service Platforms for Automated Case Assistance and Decision Support. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7744-7748.
20. Kale, P. (2024). AI-Augmented DevSecOps Pipelines for Secure and Efficient Software Delivery in Cloud-Native Platforms. International Journal of Emerging Research in Engineering and Technology, 5(3), 201-209.
21. Boyapati, P. K., & Kandula, S. T. R. (2026, March). High-Performance Distributed Deep Learning Using Adaptive Parallelism and Dynamic Workload Scheduling. In 2026 14th International Symposium on Digital Forensics and Security (ISDFS) (pp. 01-06). IEEE.
22. Gopakumar, S. (2026, January). Prescriptive Analytics in Operations: Optimizing Decisions with AI Under Uncertainty. In 2026 9th International Conference on Computational Intelligence in Data Science (ICCIDS) (pp. 1-6). IEEE.
23. Chaba, A. (2024). Unified Customer Identity and Profile Architecture for Customer Enterprise Orchestration. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9272-9285.
24. Meesala, A. (2024). Distributed securities pricing reconciliation at global scale: Price validation engine for financial institutions. World Journal of Advanced Research and Reviews, 21(2), 2212-2220.
25. Mathew, A., & Izek, B. (2026). Beyond the Pixel Veil: Forensic Analysis of IDAT Signatures and Generator-Specific Artifacts in AI-Generated Images. International Journal of Computer Technology and Electronics Communication, 9(2), 616-620.
26. Karim Chy, M. S. (2026). Federated Learning for Cross-Institutional Fraud Monitoring in National Healthcare Security. International Journal of AI, Engineering and Management Studies (IJAIEMS), 1(1), 137-155.
27. Gollapudi, R. (2026, April). An Optimization-Based Framework for Database-Level Fraud Detection in Real-Time Financial Systems. In 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 1304-1310). IEEE.
28. Juvvadi, R. R. (2018). Robotic process automation (RPA) in accounting: Measuring ROI and workforce displacement. International Journal of Research and Applied Innovations (IJRAI), 1(1), 17–21.
29. Soni, H., Veerapaneni, S. M., Sonani, R., & Thaneeru, A. (2025, November). Conversational AI Meets Dataops: Secure, Scalable NLP Architectures Using Hybrid Cloud and Multiprovider DBS. In 2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) (pp. 1-6). IEEE.
30. Narayanan, S. (2023). Operationalizing artificial intelligence security in the cloud: A practical integration framework for enterprise risk management. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10619.
31. 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
