Securing the future: AI-driven cybersecurity in the age of autonomous IoT

The Autonomous Internet of Things (A-IoT) represents a major advancement in interconnected systems, enabling self-governing smart devices to operate collaboratively across domains such as smart cities, industrial automation, healthcare, and autonomous vehicles. However, the complexity, scale, and heterogeneity of A-IoT environments introduce severe cybersecurity challenges, including expanded attack surfaces, real-time data processing demands, sophisticated adversarial threats, and privacy risks. Traditional security measures are not always adequate to address these emerging threats, and this is why intelligent adaptive defence systems are required. This narrative review offers an extensive and systematic presentation of AI-based cybersecurity strategies that are specific to the peculiarities of A-IoT ecosystems. It examines fundamental methods, including machine learning, deep learning, federated learning, and swarm intelligence, as well as the latest paradigms, such as explainable AI, generative adversarial networks, and digital twins. The approaches are discussed within the scope of the most important security tasks, such as intrusion detection, anomaly detection, malware analysis, secure authentication, and autonomous threat response. The review also locates crucial issues related to data quality, model interpretability, adversarial vulnerabilities and ethical limitations of the application of AI in security-critical applications. Moreover, it describes future research directions using hybrid AI-blockchain frameworks, self-healing autonomous agents, and trust-aware AI systems.