Autonomous Security Validation Across Cloud, Edge, And IOT Ecosystems: A Review
Abstract
Intelligent, scalable, data-driven applications have been made possible across many sectors such as healthcare, smart cities, transportation, and industrial automation thanks to the fast integration of cloud computing, edge computing, and the Internet of Things (IoT). Convergence enhances computing efficiency, real-time processing, and resource utilization; nonetheless, the scattered and diverse character of linked systems poses difficult cybersecurity issues. This review examines the fundamental security architectures of cloud, edge, and IoT systems and analyzes the security goals required to ensure confidentiality, integrity, availability, authentication, privacy preservation, accountability, and resilience. Furthermore, the study investigates the convergence of cloud–edge–IoT environments, highlighting emerging threats, vulnerabilities, and security challenges in modern interconnected infrastructures. A comprehensive review of recent literature is presented to evaluate existing approaches related to security frameworks, resource optimization, distributed computing paradigms, and IoT-cloud integration. This study highlights the need for security validation frameworks that are adaptable, AI-driven, and automated in order to provide proactive cyber defense, continuous monitoring, and threat assessment. It also identifies important research gaps in this area. Research and practitioners aiming to build next-generation cloud-edge-IoT infrastructures that are safe, robust, and trustworthy will benefit greatly from the results.
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