Transforming Enterprise Cloud Computing Through Explainable AI Driven Security Analytics and Intelligent Automation
DOI:
https://doi.org/10.21590/Keywords:
Enterprise cloud computing,, Explainable Artificial Intelligence,, Explainable AI, Security analytics,, Intelligent automation, Cloud security,, Cybersecurity, Machine learning, Threat detection, Digital transformation, Automation, Risk managementAbstract
Enterprise cloud computing has become a fundamental component of digital transformation by enabling organizations to achieve scalability, flexibility, cost efficiency, and rapid service deployment. However, the growing adoption of cloud platforms has introduced increasingly sophisticated cybersecurity threats, including advanced persistent attacks, insider threats, data breaches, and misconfigurations. Traditional security mechanisms often struggle to detect complex attack patterns and provide transparent decision-making processes. Explainable Artificial Intelligence (XAI) has emerged as a promising solution by combining the predictive capabilities of artificial intelligence with interpretable and transparent decision support. XAI-driven security analytics enables security professionals to understand how threats are detected, improves trust in automated systems, supports regulatory compliance, and facilitates faster incident response. Simultaneously, intelligent automation streamlines repetitive security operations such as threat detection, vulnerability assessment, access management, and incident remediation, thereby reducing operational costs and minimizing human error. This study explores the integration of explainable AI-driven security analytics with intelligent automation in enterprise cloud computing environments. It examines current technological developments, implementation strategies, and organizational benefits while addressing challenges related to data privacy, model transparency, scalability, and governance. The research concludes that the convergence of explainable AI and intelligent automation significantly strengthens cloud security, enhances operational resilience, and supports sustainable digital transformation across modern enterprises.
References
1. Khan, M. A., Salah, K., Jayaraman, R., Arshad, J., Omar, M., & Debe, M. (2022). Blockchain-based cloud computing: A comprehensive survey. Journal of Network and Computer Applications, 196, 103250. Elsevier. https://doi.org/10.1016/j.jnca.2021.103250
2. Soundappan, S. J. (2023). Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9074-9081.
3. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
4. Sugumar, R. (2025). Next-Generation AI Assistance Platforms for Streamlined Consumer Electronics Configuration and Adoption. International Journal of Research and Applied Innovations, 8(5), 13083-13093.
5. Mohammed, S. (2021). Hybrid cloud architecture strategy for global infrastructure operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(6), 4078–4081.
6. Umasankar, P., & Saravanan, K. (2016). Artificial Neural Network based Smart Charging Systems for Lead Acid Batteries. Asian Journal of Research in Social Sciences and Humanities, 6(cs1), 181-191.
7. Mudusu, S. K. (2022). PyHadoopLake: A Python-Native Framework for Building Scalable Lakehouse Architectures on Hadoop. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7449-7452.
8. Kanji, R. K., & Subbiah, M. K. (2024). Developing Ethical and Compliant Data Governance Frameworks for AI-Driven Data Platforms. Available at SSRN 5507919.
9. Anand, L. (2022). Integrating Kubernetes Microservices with Privileged Access Security and Real-Time Fraud Detection for Modern Enterprise Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7453-7461.
10. Veershetty, G. (2024). Robust Transition Management From Implementation to Application Management Services. Available at SSRN 6890119.
11. Challa, R. (2022). Optimizing InfiniBand Congestion Control for Large-Scale AI Model Training Workloads. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5749-5757.
12. 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.
13. 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.
14. Meesala, L. K. (2022). Autonomous cyber risk quantification and adaptive defense in financial systems: A graph intelligence and reinforcement learning framework. World Journal of Advanced Research and Reviews, 16(3), 1489-1496.
15. Raghothama Rao, G. (2024). When simplicity outscales cleverness in software architecture. Computer Fraud and Security, 2024(4). https://computerfraudsecurity.com/index.php/journal/article/view/942
16. 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.
17. Venkiteela, P. (2024). Strategic API modernization using Apigee X for enterprise transformation. Journal of Information Systems Engineering and Management, 9(4s), 14. https://jisem-journal.com/index.php/journal/article/view/13168
18. Singh, I. K. (2024). DevSecOps for enterprise ontology platforms: Continuous validation, security, and compliance of semantic knowledge systems. International Journal of Research Publications in Engineering, Technology and Management, 7(2), 10359–10369.
19. Gujarathi, M. (2022). Rule externalization for enterprise validation platforms: Architecture and design considerations. The International Journal of Research Publications in Engineering, Technology and Management, 5(4), 7163–7167.
20. 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.
21. Kumar Adabala, P. (2021). Optimizing ERP Modernization: A Smart Data Migration Framework Approach. International Journal of Enhanced Research in Science, Technology &Amp, 61-72.
22. Hossain, M. B., Rahman, R., & Hoque, K. (2021). Feature-Driven Supervised Learning for Detecting DDoS Attack. International Journal of Science and Research Archive, 4(01), 393-402.
23. Juvvadi, R. R. (2018). Robotic process automation (RPA) in accounting: Measuring ROI and workforce displacement. International Journal of Research and Applied Innovations, 1(1), 17-21.
24. Anand, L., & Neelanarayanan, V. (2020, October). Enchanced multiclass intrusion detection using supervised learning methods. In AIP conference proceedings (Vol. 2282, No. 1, p. 020044). AIP Publishing LLC.
25. 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.
26. Kale, P. (2024). A Multi-Agent AI Framework for Distributed DevOps Automation and Collaborative Decision-Making in Software Pipelines. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 274-282.
27. Chaba, A. (2020). A Reusable Enterprise Commerce Deployment Framework for Accelerated Digital Transformation. International Journal of Research and Applied Innovations, 3(2), 3068-3082.
28. Narayanan, S. (2022). Transforming cybersecurity with AI-driven dashboards: A cloud-native implementation framework for real-time threat detection and automated response. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9217.
29. Akter, K. S., Onik, T. A., Yasin, M., Nabil, M. A., Hossain, I., & Akter, S. (2024). AI-Driven Early Detection of Chronic Kidney Disease: A Comparative Benchmark of Ensemble Learning Models with Clinical Explainability. Vascular and Endovascular Review, 7(2), 480-493.
30. Vollem, S. (2018). Optimizing CI/CD pipelines for scalable enterprise cloud applications: Architecture, automation, and deployment strategies. International Journal of Scientific Research & Engineering Trends, 4(5).
31. Seetala, S. R. (2024). Architecting trustworthy AI: Governance frameworks for responsible artificial intelligence in enterprise data ecosystems. International Journal of Science, Engineering and Technology, 12(1).
32. Chaganti, S. (2024). A unified MLOps and data architecture blueprint for cross-enterprise decisioning in global financial and tourism ecosystems. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 601–611. https://ijisae.org/index.php/IJISAE/article/view/7960
33. Vasa, M. R. (2022). A Model-Driven Methodology for Customer 360 Data Modernization: Conceptual-to-Physical Data Modeling for Multi-Use-Case Cloud Analytics. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9612.
34. 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.
35. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
36. Kumar, R., Tripathi, R., Marchang, N., & Srivastava, G. (2022). Machine learning approaches for secure cloud computing: A survey. Journal of Cloud Computing, 11, 91. Springer. https://doi.org/10.1186/s13677-022-00352-2
