<![CDATA[The rapid expansion of the Internet of Things (IoT) has transformed industries but also heightened cybersecurity vulnerabilities. Cyber threats, including ransomware, data breaches, and distributed denial-of-service (DDoS) attacks, increasingly jeopardize critical infrastructure. Traditional security methods, such as encryption and firewalls, often fail to counter evolving AI-driven threats. This study introduces an AI-based security model that integrates deep learning and federated learning for real-time IoT threat detection and mitigation. The proposed system employs a hybrid CNN-LSTM architecture to analyze network traffic, while federated learning enhances detection accuracy and ensures data privacy. Experimental results demonstrate 92% detection accuracy, 4.2% false positive rate, and latency under 50 ms, outperforming conventional rule-based systems. Additionally, integrating AI with IoT protocols like MQTT and CoAP optimizes processing for low-power devices. The study highlights regulatory challenges, as 73% of industrial organizations lack AI-driven security policies. The proposed framework aligns with NIST SP 800-82 and GDPR, ensuring scalable and adaptive industrial cybersecurity solutions. These findings contribute to developing AI-driven security strategies, providing a foundation for enhancing IoT resilience against evolving cyber threats.]]>
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