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@article{187743,
        author = {Hemamalini M and L. Suganthi and K. Kalaivani and P. Dhivya Bharathi},
        title = {Convergence of AI and Cloud Security: A Systematic Review of Techniques, Challenges, and Future Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {7040-7046},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187743},
        abstract = {The rapid adoption of cloud computing has introduced complex security challenges that traditional methods struggle to address. This paper presents a systematic review of the convergence of Artificial Intelligence (AI) and cloud security. We analyze contemporary research to catalog and evaluate AI-driven techniques including machine learning for anomaly detection, deep learning for threat intelligence, and reinforcement learning for automated response that enhance the protection of cloud environments. The review identifies significant challenges, such as data privacy in centralized training, model interpretability for compliance, and adversarial attacks on AI systems themselves. Furthermore, we discuss the resource overhead of deploying these models at scale. Finally, the paper outlines critical future directions, emphasizing the need for explainable AI (XAI), federated learning for privacy preservation, and robust adversarial defense mechanisms to secure the next generation of cloud infrastructures.},
        keywords = {Artificial Intelligence (AI), Cloud Security, Systematic Review, Machine Learning, Anomaly Detection, Explainable AI (XAI), Automated Incident Response, Adversarial Attacks},
        month = {November},
        }