How AI can be Powerful in Providing Security for IoT

The proliferation of the Internet of Things (IoT) globally has revolutionized industries such as healthcare, smart cities, agriculture, and manufacturing. However, this widespread integration has brought along critical security concerns, making IoT networks vulnerable to attacks such as data breaches, spoofing, eavesdropping, and Distributed Denial of Service (DDoS). Traditional security mechanisms struggle to scale and adapt to the heterogeneous, dynamic nature of global IoT systems. Artificial Intelligence (AI) emerges as a powerful tool capable of enhancing IoT security by enabling adaptive, real-time threat detection, self-learning models, and automated response mechanisms. This research explores the role of advanced AI techniques—such as federated learning, deep neural networks, reinforcement learning, and anomaly detection models—in securing IoT infrastructures across various domains. The paper provides a detailed literature review of current trends and studies, analyzes the effectiveness of AI in identifying threats, and discusses its limitations, ethical implications, and global deployment challenges. The findings indicate that AI-driven IoT security frameworks significantly outperform traditional systems in scalability, responsiveness, and contextual awareness, making AI a pivotal force in the future of global cybersecurity.