Risk Assessment and Risk Management Challenges in Intelligent IoT-Based Smart City Infrastructures

Smart Internet of Things (IoT) technologies, which include artificial intelligence (AI) are increasingly being implemented in the infrastructures of smart cities to enhance the efficiency, sustainability, and service delivery of cities. Nonetheless, with the implementation of such intelligent and interconnected systems, there are complex security, privacy, and safety risks that are not easily handled against conventional risk assessment and risk management methods. Current frameworks tend to be unresponsive and centralized whereas smart city infrastructures are dynamic, decentralized, and based on autonomous decision-making elements. This incompatibility poses serious problems for the reliability and robustness of intelligent urban systems. The study contains a systematic literature review of the risk assessment and risk management issues in smart city infrastructure based on intelligent IoT. The review is based on security, privacy, safety, and risks of AI, such as adversarial machine learning, data poisoning, model drift, and failures of autonomous systems. To enhance the level of methodological transparency, the review lists the databases searched, search words, screening process, inclusion, and exclusion criteria, and the resulting list of studies selected. The comparison of the key risk assessment methods, such as the standard-based, qualitative, probabilistic, and AI-based approaches, has been made, and their advantages and weaknesses in the smart city context have been identified. The results indicate that existing frameworks are still in fragments and not always able to deal with the joint effect of the heterogeneity of IoT, AI-based decisions, cyber-physical interdependence, scalability, and governance. The study, based on this summary, offers a more defined taxonomy of risk factors and research directions towards adaptive, AI-conscious and operationally feasible risk management in smart cities.