As artificial intelligence introduces new risks, some potentially catastrophic or even existential, there is little data or detailed theory to assess them. Policymakers often resort to expert best guesses for the probability of doom but probability is not always the most appropriate tool, especially for the types of uncertainties in AI risk. This report details a brief introduction to Belief and Plausibility, which provides an alternative approach that is mathematically rigorous, uses familiar vocabulary, and only requires policymakers to ask two simple questions.

In a new Security Studies article, Renee DiResta and Josh A. Goldstein lay out how state-backed propagandists run “full-spectrum” propaganda campaigns, relying on overt and covert tools across broadcast and social media.

Organizations face growing pressure to adopt artificial intelligence, but often lack practical guidance on how to do so effectively. This report bridges the gap between high-level principles and real-world implementation, offering actionable steps across the AI adoption life cycle. Drawing on over 1,200 resources, this reference guide provides practitioners with the knowledge required to operationalize AI safety, security, and governance practices within their organizations.

Leading artificial intelligence companies have started to use their own systems to accelerate research and development, with each generation of AI systems contributing to building the next generation. This report distills points of consensus and disagreement from our July 2025 expert workshop on how far the automation of AI R&D could go, laying bare crucial underlying assumptions and identifying what new evidence could shed light on the trajectory going forward.

This report analyzes over 250 scientific publications that use open language models in ways that require access to model weights and derives a taxonomy of use cases that open weights enable. The authors identified a diverse range of seven open-weight use cases that allow researchers to investigate a wider scope of questions, explore more avenues of experimentation, and implement a larger set of techniques.

Organizations looking to adopt artificial intelligence (AI) systems face the challenge of deciphering a myriad of voluntary standards and best practices—requiring time, resources, and expertise that many cannot afford. To address this problem, this report distills over 7,000 recommended practices from 52 reports into a single harmonized framework. Integrating new AI guidance with existing safety and security practices, this work provides a road map for organizations navigating the complex landscape of AI guidance.

This roundtable report explores how practitioners, researchers, educators, and government officials view work-based learning as a tool for strengthening the cybersecurity workforce. Participants engaged in an enriching discussion that ultimately provided insight and context into what makes work-based learning unique, effective, and valuable for the cyber workforce.

System-to-model innovation is an emerging innovation pathway in artificial intelligence that has driven progress in several prominent areas over the last decade. System-level innovations advance with the diffusion of AI and expand the base of contributors to leading-edge progress in the field. Countries that can identify and harness system-level innovations faster and more comprehensively will gain crucial economic and military advantages over competitors. This paper analyzes the benefits of system-to-model innovation and suggests a three-part framework to navigate the policy implications: protect, diffuse, and anticipate.

Artificial intelligence (AI) is beginning to change cybersecurity. This report takes a comprehensive look across cybersecurity to anticipate whether those changes will help cyber defense or offense. Rather than a single answer, there are many ways that AI will help both cyber attackers and defenders. The report finds that there are also several actions that defenders can take to tilt the odds to their favor.

We investigate the scale of attack and defense mathematically in the context of AI's possible effect on cybersecurity. For a given target today, highly scaled cyber attacks such as from worms or botnets typically all fail or all succeed.