AI-Driven Risk Management Framework for Decentralized IoT Systems: Integrating Blockchain Technology for Enhanced Security and Trust

The proliferation of Internet of Things (IoT) devices has introduced unprecedented challenges in risk management, particularly in decentralized systems where traditional approaches fall short. This study proposes a novel, integrated framework that leverages Artificial Intelligence (AI), blockchain technology, and decentralized IoT architectures to address these challenges. The primary objective is to enhance the security, reliability and adaptability of risk management in complex IoT ecosystems. The methodology combines deep learning algorithms for anomaly detection and risk prediction with blockchain-based smart contracts for automated policy enforcement. A decentralized consensus mechanism optimized for resource-constrained IoT devices and utilize federated learning to preserve data privacy while improving model accuracy. Extensive simulations conducted on both synthetic and real-world datasets demonstrate significant improvements over traditional risk management approaches. The proposed model demonstrated significant improvements in anomaly detection, risk prediction, and system responsiveness. The integration of blockchain technology effectively enhanced data integrity and trust across the decentralized IoT environment. A smart city case study validated the framework's practical applicability, highlighting its potential to improve operational efficiency and resilience in real-world scenarios. Overall, the system exhibited robust performance under various threat conditions, surpassing traditional approaches in both accuracy and adaptability. This research contributes to the field by providing a scalable, secure, and adaptive risk management solution for decentralized IoT systems. The integration of AI and blockchain not only enhances current capabilities but also paves the way for more robust and trustworthy IoT applications across various sectors.