Artificial Intelligence-Driven Threat Detection in Sustainable Smart Cities: A Case Study for Saudi Urban Infrastructure

Artificial Intelligence-driven detection mechanisms present both opportunities and challenges in modern systems, particularly within smart cities that rely on complex operational technologies. In the context of Saudi urban infrastructure, rapidly evolving and multidimensional cyber threats require advanced, energy-efficient security solutions. This research proposes an Artificial Intelligence-based Threat Detection Mechanism designed to proactively identify and mitigate cyber threats while maximizing energy efficiency and minimizing cost and system complexity. Purpose: The proposed theoretical framework focuses on securing sustainable smart cities by integrating Artificial Intelligence-based anomaly detection with quantum-enhanced algorithms to address high-dimensional and emerging cyber threats across interconnected urban systems. The Artificial Intelligence-based Threat Detection Mechanism enables early and proactive threat detection across sustainable smart city networks, including connections to external and global infrastructures, ensuring continuous monitoring, resilience, and service continuity. Methods: The methodology emphasizes the development of energy-efficient Artificial Intelligence models and quantum protocols, incorporating intelligent risk assessment, adaptive calibration, and automated response mechanisms. In addition, the framework introduces distributed security hubs to enhance cybersecurity robustness and scalability. Anticipated Results and Conclusions: Anticipated outcomes include improved security management policies, automated threat detection and response, and adaptive protection against evolving cyber risks. The proposed framework provides a scalable and cost-effective solution aligned with sustainability objectives. Ultimately, this research contributes a proactive and intelligent framework for securing smart city ecosystems, supporting long-term development goals and aligning with Saudi Vision 2030.