Adaptive Security and Privacy Management for Cloud APIs: A Comprehensive Review

The problem of security and privacy is relevant in an era of cloud computing; cloud APIs are cloud interfaces for accessing cloud services and as such they are responsible for providing organization security and privacy. In this paper, we provide a comprehensive review of the work on the design of adaptive security and privacy management methodologies in cloud environments. In particular we study AI-based threat detection and privacy preserving frameworks for cloud APIs. Previous review combines 30 recent research papers that analyze the use of methodologies for real time threat detection, privacy risk management, and means of integrating AI automatic tools in network security. We also discuss how encryption techniques, federated learning, and homomorphic encryption can protect sensitive data specifically in distributed settings across multiple clouds. The research identifies important research gaps and challenges as well as suggests future directions for the development of more robust and dynamic systems. The goal of this work is to lay the foundation of future research and development on the adaptive security and privacy management for cloud APIs, focusing on the practical, scalable, and secure solutions.