Cybersecurity Risk Management in Smart IoT Ecosystems Using Artificial Intelligence

Abstract

Faster evolution of IoT (Internet of Things) has changed the course of today's digital world fostering the interconnection of smart devices, intelligent automation, and real-time data sharing across various industries. Smart IoT ecosystems are becoming more and more embedded in healthcare, transportation, manufacturing, smart cities, energy infrastructures, retail and industrial. Internet of Things (IoT) technologies offer operational efficiency, scalability and automation, but they also bring with them many cybersecurity problems, such as a wide attack surface, weak authentication mechanisms, insecure communication protocols, device heterogeneity and resource constraint. Traditional cyber-security approaches are not enough to deal with the highly dynamic and complex cyber threats directed to the IoT infrastructure. Thus, organizations have turned to adopting artificial intelligence (AI) powered cybersecurity risk management frameworks for enhanced threat detection, predictive analytics, anomaly identification, incident response and resilience.

This review article presents a critical analysis of Cyber Security Risk Management in smart IoT ecosystem through AI. It delves into IoT cybersecurity concepts, AI-powered threat detection systems, machine learning applications, behavioral analytics, smart intrusion detection systems, predictive risk management, blockchain integration, cloud–edge security models, and Zero Trust security models. In addition, the article covers some of the most significant cyber threats to IoT, such as botnets, ransomware, DDoS, data breaches, insider threats, and AI-driven attacks.

The study highlights the importance of machine learning, deep learning, reinforcement learning, natural language processing and federated learning in the context of AI technologies for boosting IoT security infrastructures. All the ethical issues, privacy concerns, threat from malicious AI, workforce concerns and regulations are explored in depth. Moreover, innovations that are new to the industry, such as digital twins, explainable AI, autonomous security systems and quantum-resistant cybersecurity, are all mentioned.

Results of the analysis show that the use of AI in cybersecurity risk management greatly contributes to the security, resilience, and adaptability of smart IoT ecosystems. But to make them work successfully, it is important to have robust governance structures, ethics committees, human-AI partnerships, and ongoing supervision. The authors conclude that cybersecurity strategies leveraging AI are crucial for safeguarding modern smart ecosystems and promoting sustainable digital transformation.