Proceedings of 1st GENZERO Workshop: Revolutionizing Autonomous...

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Proceedings of 1st GENZERO Workshop: Revolutionizing Autonomous Systems Security with Generative AI, and Large Language Models for Zero Trust Architecture.

This Open Access volume brings together peer-reviewed research presented at the GENZERO Workshop on Security and Autonomous Systems, held at the Technology Innovation Institute in Abu Dhabi in November 2024. As one of the leading forums in the fields of autonomous system security and generative AI, the workshop focuses on advancing Zero Trust principles across a wide range of intelligent and self-operating platforms.

At the heart of this collection is a growing concern: as autonomous systems become more capable and widespread, they also become more vulnerable. The volume highlights the urgent need to secure so-called “Physical AI” systems—including drones, unmanned ground vehicles (UGVs), humanoid robots, and other autonomous machines—that operate in real-world environments where cyberattacks could lead to serious physical and operational consequences.

The contributions in this proceedings explore both foundational theory and practical solutions for strengthening the safety and resilience of these systems. Researchers present new models, architectures, and frameworks that integrate large language models and generative AI techniques to enhance security, coordination, and intelligence in autonomous networks. Special attention is given to drone swarms, robotic systems, and distributed AI agents that must operate reliably in dynamic and potentially hostile environments.

As these technologies evolve, security is no longer a secondary concern but a core design requirement. The papers in this volume investigate advances in distributed intelligence, secure communication protocols, adversarial robustness, and real-time anomaly detection. Together, they offer practical strategies relevant to aerospace, defense, critical infrastructure, and other high-stakes industries where system failure is not an option.

The volume also includes forward-looking discussions on how machine learning and AI-driven approaches are reshaping traditional ideas of safety, trust, and control in autonomous decision-making systems. These perspectives examine how continuous operation, adaptive learning, and real-time responsiveness can be achieved without compromising security or reliability.

By combining cutting-edge research with applied insights, this collection provides a valuable resource for researchers, engineers, policymakers, and industry professionals working in the rapidly expanding field of autonomous systems. It not only documents current progress but also highlights the emerging challenges and opportunities that will define the future of secure, intelligent, and trustworthy autonomous technologies.