Smart cities integrate interconnected technologies to enhance urban living through efficient infrastructure and services, yet this technological evolution introduces significant cybersecurity vulnerabilities that threaten critical urban systems. AI-driven security architectures emerge as sophisticated solutions, utilizing machine learning algorithms and predictive analytics to provide real-time threat detection, automated incident response, and proactive defense mechanisms against cyber-attacks. These intelligent systems process vast amounts of data from sensors, cameras, traffic networks, and utility systems to maintain the integrity and availability of essential urban services. While AI-driven security delivers substantial benefits, including enhanced public safety, service continuity, and economic protection, it raises profound privacy concerns and ethical challenges related to surveillance, algorithmic bias, and data misuse. Implementing privacy-preserving technologies such as federated learning and differential privacy, with transparent governance frameworks and public engagement initiatives, offers pathways to balance security effectiveness with individual rights protection. Future developments in explainable AI, quantum-resistant algorithms, and interdisciplinary collaboration will be crucial for creating equitable and trustworthy AI-driven security systems that serve urban communities while preserving democratic values and social equity.
Keywords: AI-driven security, Cybersecurity, IoT networks, privacy preservation, smart cities
