AI-Based Autonomous Security for Cyber–Physical Systems 2.0 in IoT Ecosystems—A Narrative Review
TL;DR
How artificial intelligence (AI), machine learning (ML), and generative AI (GenAI) enhance real-time threat detection, prediction, and response in distributed environments in distributed environments is explored, and the role of edge computing in decentralizing intelligence is highlighted, thereby reducing latency and limiting exposure of sensitive data.
Abstract
This narrative review examines the evolving landscape of AI-based security in Cyber–Physical Systems 2.0 (CPS 2.0) within the context of AI-driven autonomous cybersecurity solutions for the Internet of Things (IoT). This article presents a narrative review, supported by a structured literature search inspired by the PRISMA 2020 project and descriptive publication statistics. It combines transparent study selection with qualitative conceptual synthesis, rather than a formal systematic review or bibliometric analysis. CPS 2.0 represents a new generation of interconnected systems that tightly integrate physical processes with intelligent computational components, enabling increased autonomy and operational efficiency. However, this growing complexity introduces advanced security threats and privacy challenges that traditional centralized security frameworks are ill-equipped to address due to limitations in scalability, latency, and data sensitivity. The paper explores how artificial intelligence (AI), machine learning (ML), and generative AI (GenAI) enhance real-time threat detection, prediction, and response in distributed environments. It highlights the role of edge computing in decentralizing intelligence, thereby reducing latency and limiting exposure of sensitive data. Additionally, federated learning (FL) is discussed as a privacy-preserving paradigm that enables collaborative model training across distributed nodes without sharing raw data. The integration of GenAI, FL, and edge computing is presented as a synergistic approach that enables adaptive, context-aware, and proactive defense mechanisms against dynamic and evolving cyber threats. The review further analyzes architectural frameworks, key advantages, and inherent vulnerabilities of CPS 2.0, along with mitigation strategies and real-world applications, particularly in industrial control systems. By synthesizing current advancements and challenges, this work provides a comprehensive roadmap for designing resilient, scalable, and privacy-aware CPS infrastructures. The findings contribute to the development of secure and intelligent systems aligned with the future demands of Industry 4.0, 5.0, and beyond.