Multi-Agent AI Systems for Coordinated Cybersecurity in Smart Cities

The rapid digital transformation of urban environments into smart cities has significantly increased reliance on interconnected systems, making them prime targets for sophisticated cyber threats. Traditional cybersecurity mechanisms often fail to provide adequate real-time responsiveness and scalability required to protect complex, heterogeneous infrastructures. This paper explores the implementation of Multi-Agent Artificial Intelligence (AI) Systems as a coordinated cybersecurity solution for smart cities. By leveraging autonomous, intelligent agents capable of perceiving, learning, and responding to diverse threats across critical sectors such as transportation, energy, and public safety, Multi-Agent Systems (MAS) offer a decentralized, adaptive defense mechanism. The study presents a layered MAS architecture designed to enhance threat detection, anomaly classification, and rapid incident response through inter-agent communication and AI-driven decision-making. Comparative simulation results demonstrate the effectiveness of this approach over conventional rule-based systems in terms of detection accuracy, response latency, and scalability. The findings contribute to the development of intelligent, resilient cybersecurity frameworks that align with the dynamic needs of smart urban ecosystems.