Smart cities rely on the convergence of Artificial Intelligence (AI), 6G communication networks, and Internet of Things (IoT) devices to enhance urban services, optimize resource management, and provide data-driven decision-making. The integration of these technologies introduces complex cybersecurity threats and data privacy challenges, arising from heterogeneous IoT devices, high-speed communication infrastructures, and AI-driven analytics. This chapter examines critical vulnerabilities in AI–6G–IoT ecosystems, including network intrusions, adversarial attacks on AI models, and risks associated with large-scale data aggregation. State-of-the-art security mechanisms and privacy-preserving techniques are discussed, encompassing blockchain-based frameworks, federated learning, differential privacy, homomorphic encryption, and edge-enabled security solutions. Regulatory compliance, citizen data ownership, and adaptive access control models are analyzed to ensure secure and trustworthy smart city operations. The chapter further outlines emerging research directions for scalable, energy-efficient, and quantum-resistant cybersecurity strategies capable of supporting resilient, privacy-aware urban infrastructures. Findings provide valuable insights for researchers, urban planners, and policymakers aiming to safeguard smart city networks against evolving technological and regulatory challenges.
Smart cities leverage the convergence of Artificial Intelligence (AI), 6G communication networks, and Internet of Things (IoT) devices to enhance urban living, optimize resource management, and enable data-driven decision-making. IoT devices deployed across transportation systems, healthcare services, energy grids, and environmental monitoring platforms generate continuous streams of heterogeneous data [1]. AI algorithms process these datasets to identify patterns, predict urban trends, and automate critical decisions, improving efficiency, reliability, and responsiveness in city operations [2,3]. 6G networks provide the ultra-high-speed, low-latency communication infrastructure necessary for seamless interaction among millions of connected devices, enabling near real-time data transmission and analytics [4,5].
The integration of AI and IoT with next-generation networks introduces multifaceted cybersecurity challenges [6]. Attackers can exploit vulnerabilities in IoT devices, communication protocols, and AI models to disrupt operations or gain unauthorized access to sensitive data [7]. Cyber threats such as adversarial attacks on AI systems, distributed denial-of-service (DDoS) attacks on network nodes, and malware infiltration in IoT devices can compromise the integrity and availability of essential urban services [8]. Traditional security measures, designed for isolated networks or slower communication channels, often prove inadequate for handling the scale, speed, and complexity of AI–IoT–6G ecosystems, necessitating innovative and adaptive cybersecurity frameworks [9, 10].
Data privacy constitutes a significant concern within smart city ecosystems. IoT devices and AI analytics collect and process personal, financial, and health-related information, creating potential exposure to unauthorized access, data breaches, or misuse [11]. Aggregation of large-scale datasets amplifies the risk of re-identification, while centralized storage models increase vulnerability to attacks [12]. Privacy-preserving techniques such as federated learning, homomorphic encryption, and differential privacy allow AI systems to extract insights without compromising individual confidentiality [13]. Implementation of robust access control policies ensures that only authorized entities interact with sensitive data, strengthening citizen trust in urban technologies [14, 15].
