Abstract

With more and more companies moving to cloud platforms, adequate cloud security is the topmost priority for organizations today. Conventional security tools never identify sophisticated cyber-attacks, and thus AI-based real-time anomaly detection is the need of the hour. This research investigates the application of cutting-edge machine learning, deep learning, and security analytics in identifying and handling security anomalies from cloud logs. Our methodology utilizes hybrid AI models, federated learning, and graph neural networks to provide more accurate detection without breaching data privacy. Furthermore, the use of quantum-resilient cryptographic models and zero-trust principles further enhances cloud security. Cloud-native scalable technologies, decentralized security models, and real-time automated incident response systems are also utilized in this research, and hence, it is an end-to-end, adaptive, and high-performance security solution for multi-clouds