AI-Based Zero Trust Architecture for Cognitive City Networks
Mohammed Ishaque, Hanouf Albatati, Mostafa Nofal · 2025
The rapid development of cognitive cities, which leverage artificial intelligence (AI) and interconnected smart systems, introduces significant security challenges due to their complex and dynamic infrastructure. Traditional security models that rely on fixed perimeters and implicit trust are insufficient to protect the diverse range of services, from smart transportation to IoT-powered utilities, in cognitive city environments. In this paper, we propose an AI-Based Zero Trust Architecture (ZTA) designed to secure the multilayered, interconnected networks of cognitive cities. Our framework assumes that no entity, human or machine, can be trusted by default and integrates AI-driven mechanisms for continuous authentication, realtime access control, and proactive threat detection. The proposed AI-powered ZTA utilizes dynamic authentication protocols based on multi-factor and behavioral biometrics to constantly verify identities. It incorporates AI models for context-aware access control, continuously adapting security policies in real time based on user behavior, environmental conditions, and risk factors. Additionally, AI enhances micro-segmentation, enabling the city's network to be divided into smaller, isolated segments to contain potential threats. Through real-time monitoring and AI-driven anomaly detection, our architecture continuously scans for abnormal activities across IoT networks, smart infrastructure, and critical services, proactively mitigating threats before they escalate. AI's predictive capabilities also play a key role in anticipating insider threats and ensuring device trustworthiness by analyzing communication patterns and security vulnerabilities in endpoints. This paper presents a detailed system design, outlining how AI models can be integrated into Zero Trust principles to provide scalable and adaptive security for cognitive cities. We demonstrate the application of our framework through several use cases, including autonomous vehicle networks, smart energy grids, and IoT-based healthcare systems. Moreover, we address challenges such as scalability, real-time decisionmaking, and privacy concerns. Our findings suggest that an AI-enhanced Zero Trust Architecture not only strengthens the cybersecurity posture of cognitive cities but also offers a unified, automated, and selfadaptive framework capable of responding to evolving threats in real time. This research contributes to the advancement of smart city security by presenting an innovative approach that merges AI with Zero Trust principles, offering a robust solution for safeguarding future urban infrastructures.