The field of artificial intelligence is moving towards a greater emphasis on security and resilience, with a focus on developing robust and trustworthy systems. Researchers are exploring new frameworks and methodologies for evaluating and improving the robustness and resilience of AI agents, particularly in high-risk sectors such as congestion management and critical infrastructure. The development of novel metrics and threat models is also a key area of research, with a focus on proactive analysis and least privilege enforcement. Furthermore, there is a growing recognition of the importance of language and governance in shaping the development and deployment of AI systems, with a need for a more precise and inclusive lexicon to support transparent and equitable regulatory frameworks. Noteworthy papers in this area include:

• A paper introducing a novel framework for quantitatively evaluating the robustness and resilience of reinforcement learning agents in congestion management.
• A paper proposing a hypervisor architecture for sandboxing powerful AI models and mitigating existential risks.
• A paper advocating for a security-first approach to AI development, with a focus on core threat models and emerging defense mechanisms.