Intelligent Cloud Security Architectures Using Artificial Intelligence and Federated Learning Techniques
DOI:
https://doi.org/10.15662/IJARCST.2026.0901005Keywords:
Cloud Security, Artificial Intelligence, Federated Learning, Data Privacy, Intrusion Detection, Cybersecurity, Distributed Systems, Machine Learning, Threat Intelligence, Secure ArchitectureAbstract
The rapid adoption of cloud computing has transformed modern digital infrastructure, enabling scalable, flexible, and cost-efficient services. However, this transformation has also introduced complex security challenges, including data breaches, insider threats, and sophisticated cyberattacks. Traditional security mechanisms often fail to address these evolving threats due to their static and reactive nature. This paper explores intelligent cloud security architectures that leverage Artificial Intelligence (AI) and Federated Learning (FL) techniques to enhance threat detection, privacy preservation, and system resilience. AI-driven models enable real-time anomaly detection, predictive threat analysis, and automated response mechanisms, significantly improving security efficiency. Meanwhile, Federated Learning facilitates collaborative model training across distributed cloud environments without sharing sensitive data, thereby preserving privacy and ensuring regulatory compliance. The integration of AI and FL creates a decentralized, adaptive, and robust security framework capable of addressing emerging cyber risks. This study examines the design principles, architecture components, and operational workflows of such systems. Additionally, it evaluates their effectiveness compared to traditional approaches and identifies implementation challenges. The findings suggest that intelligent cloud security architectures can significantly improve both security posture and data privacy, making them a promising solution for next-generation cloud environments.References
1. Murugeshwari, B., Selvaraj, D., Sudharson, K., & Radhika, S. (2023). Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography. Intelligent Automation & Soft Computing, 35(1).
2. Sugumar, R. (2025). Federated AI in Offline-First Mobile Health Architectures for Privacy-Preserving Clinical Intelligence. International Journal of Science, Research and Technology, 8(4), 14589-14600.
3. Adepu, G. (2025). AI-based epidemiological data platforms for early outbreak detection and real-time health analytics. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 9–29.
4. Mathew, A., & Alex, H. (2022). Detect & protect-medical device cybersecurity. Curr. Overview Sci. Technol. Res, 1, 60-68.
5. Sammy, F., Chettier, T., Boyina, V., Shingne, H., Saluja, K., Mali, M., ... & Shobana, A. (2025). Deep Learning-Driven Visual Analytics Framework for Next-Generation Environmental Monitoring. Journal of Applied Science and Technology Trends, 114-122.
6. Vigenesh, M. (2025). Autonomous Operational Resilience across AI Guided Cloud Platforms with Proactive Threat Mitigation. International Journal of Technology, Management and Humanities, 11(03), 108-115.
7. Soundappan, S. J. (2025). Next Generation AI Enabled Holistic Cognitive Platform for Secure Cloud Network Intelligence Enterprise Systems and Digital Trust Optimization. International Journal of Computer Technology and Electronics Communication, 8(5), 11534-11542.
8. 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.
9. G. Vimal Raja, K. K. Sharma (2015). Applying Clustering technique on Climatic Data. Envirogeochimica Acta 2 (1):21-27.
10. Umasankar, P. (2025). Advanced Unified AI Cognitive Ecosystem for Adaptive Cloud Network Security Intelligent Enterprise Transformation and Self Healing Data Infrastructure. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 11209-11217.
11. Vankayala, S. C. (2023). Governed Autonomy in Reliability Engineering: Integrating Error Budgets with AI-Driven Remediation. J Artif Intell Mach Learn & Data Sci 2023, 1(2), 3191-3196.
12. Gopinathan, V. R. (2025). Software engineering practices for AI-driven systems: From development to deployment (MLOps perspective). International Journal of Science, Research and Technology, 8(1), 13493-13500.
13. Narayanan, S. (2025). Autonomous cyber sovereignty: A dual-control architecture for agentic artificial intelligence in offensive defensive security ecosystems. World Journal of Advanced Research and Reviews, 25(3), 2538–2546.
14. Alam, M. K., & Fahad, M. L. R. (2022). The Digital Shield: An Analysis of AI's Role in Protecting US Financial Infrastructure from Cyberattack. Journal of Computer Science and Technology Studies, 4(1), 112-133.
15. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
16. Raja, G. V. (2023). Modernizing Enterprise Systems using AI with Machine Learning and Cloud Computing for Intelligent Systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
17. Mudusu, S. K. (2025). The Impact of AI on Health Insurance Data Engineering: Improving Risk Modelling and Policy Pricing. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 13(1), 99-107.
18. Boddupally, H. L. (2018). Secure data governance for enterprise reporting: A governance-layer model for SSRS-based architectures. Journal of Artificial Intelligence, Machine Learning & Data Science, 1(1), 3148-3153.
19. Rengarajan, A., Mishra, A., Kulhar, K. S., Shrivastava, V. P., & Alawneh, Y. J. J. (2024, March). Role of Deep Reinforcement Learning in Mitigating Cyber Security Issues: A Review. In International Conference on Renewable Power (pp. 37-48). Singapore: Springer Nature Singapore.
20. Tiwari, S. K. (2025). Automating Behavior-Driven Development with Generative AI: Enhancing Efficiency in Test Automation. Frontiers in Emerging Computer Science and Information Technology, 2(12), 01-14.
21. Gentyala, R. (2023). Beyond Syntax: A Framework for Semantically-Aware Verification Rules in Multi-Domain Data Cleansing. Journal of Scientific and Engineering Research, 10(3), 160-174.
22. Soundappan, S. J. (2022). AI-Based Fault Detection and Isolation for Reliability in Modern Power Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106-7110.
23. Parupalli, A. (2025, November). Optimizing Customer Engagement in Multi-source E-commerce Retail Datasets Using Artificial Intelligence-Based Efficient Techniques. In 2025 5th International Conference on Artificial Intelligence and Signal Processing (AISP) (pp. 1-7). IEEE.
24. Yamsani, N. (2020). Architecting Enterprise-Wide Master Data Platforms for Cloud-Enabled Organizations Using EBX-Centered Governance and Integration Design. European Journal of Advances in Engineering and Technology, 7(8), 150-162.
25. Mohammad Kowshik, A., Md Lutfur Rahman, F., & Nayem, M. (2024). Guardian of the Vault: The Development of AI-Driven Solutions for Protecting Sensitive Financial Data in the US. Guardian of the Vault: The Development of AI-Driven Solutions for Protecting Sensitive Financial Data in the US, 7(2), 219-249.
