AI-Driven Cloud Security Frameworks: Techniques, Challenges, and Lessons from Case Studies

Abstract

This paper explores the design, implementation, and practical implications of AI-driven cloud security frameworks. As cloud infrastructures continue to grow in complexity, traditional security mechanisms often fall short in detecting and mitigating sophisticated, evolving threats. By analyzing a wide range of AI techniques—such as supervised and unsupervised learning, deep learning, natural language processing, reinforcement learning, and federated learning—this study demonstrates how these tools enhance threat detection, policy automation, and data protection. A multi-layered architectural model is proposed, incorporating data collection, preprocessing, AI modeling, decision-making, and feedback mechanisms. The paper also discusses key challenges, including data quality, adversarial attacks, explainability, latency, compliance, and scalability. Through four detailed case studies from Microsoft Azure, AWS, Capital One, and Alibaba Cloud, the work identifies valuable lessons such as the need for hybrid AI-rule systems, the impact of automation on response time, the importance of interpretability tools, and the role of federated learning in regulatory compliance. These findings offer actionable insights for designing robust and adaptive cloud security infrastructures that align with both operational needs and regulatory frameworks.