This Open Access volume compiles peer-reviewed research at the GENZERO Workshop on Security and Autonomous Systems, hosted by the Technology Innovation Institute in Abu Dhabi in November 2024. As a leading event in the fields of autonomous systems and generative AI, the workshop emphasizes the expansion of Zero Trust architecture across a variety of autonomous platforms. It highlights the critical importance of protecting Physical AI systems--such as drones, unmanned ground vehicles (UGVs), and humanoid robots--from cyberattacks, which have the potential to cause significant harm. The proceedings feature theoretical models, practical frameworks, and real-world applications of large language models and other generative AI techniques aimed at enhancing security, resilience, and intelligence in UAV collectives, robotics, and other autonomous systems. Ensuring security has become a paramount challenge as these systems become increasingly complex and self-directed. The studies presented here focus on breakthroughs in distributed intelligence, secure communications, and real-time anomaly detection, offering actionable strategies for aerospace, defense, and critical infrastructure industries. Forward-looking discussions also explore how machine learning and AI-driven approaches could redefine safety and security standards, particularly in autonomous decision-making and continuous operations. Collectively, this volume provides invaluable insights for researchers, practitioners, and policymakers working toward robust and trustworthy autonomous solutions while also addressing pressing challenges and emerging opportunities in this rapidly evolving field.

This Open Access volume compiles peer-reviewed research at the GENZERO Workshop on Security and Autonomous Systems, hosted by the Technology Innovation Institute in Abu Dhabi in November 2024. As a leading event in the fields of security of autonomous systems and generative AI, the workshop emphasizes the expansion of Zero Trust architecture across a variety of autonomous platforms. It highlights the critical importance of protecting Physical AI systems--such as drones, unmanned ground vehicles (UGVs), and humanoid robots--from cyberattacks, which have the potential to cause significant harm. The proceedings feature theoretical models, practical frameworks, and real-world applications of large language models and other generative AI techniques aimed at enhancing security, resilience, and intelligence in UAV collectives, robotics, and other autonomous systems. Ensuring security has become a paramount challenge as these systems become increasingly complex and self-directed. The studies presented here focus on breakthroughs in distributed intelligence, secure communications, and real-time anomaly detection, offering actionable strategies for aerospace, defense, and critical infrastructure industries. Forward-looking discussions also explore how machine learning and AI-driven approaches could redefine safety and security standards, particularly in autonomous decision-making and continuous operations. Collectively, this volume provides invaluable insights for researchers, practitioners, and policymakers working toward robust and trustworthy autonomous solutions while also addressing pressing challenges and emerging opportunities in this rapidly evolving field.