CSET’s Jessica Ji shared her expert analysis in an interview published by Science News. The interview discusses the U.S. government’s new action plan to integrate artificial intelligence into federal operations and highlights the significant privacy, cybersecurity, and civil liberties risks of using AI tools on consolidated sensitive data, such as health, financial, and personal records.

AI has the potential to transform cybersecurity through automation of vulnerability discovery, patching, and exploitation. Integrating these models with traditional software security tools allows engineers to proactively secure and harden systems earlier in the software development process.

Frontier AI capabilities show no sign of slowing down so that governance can catch up, yet national security challenges need addressing in the near term. This blog post outlines a governance approach that complements existing commitments by AI companies. This post argues the government should take targeted actions toward AI preparedness: sharing national security expertise, promoting transparency into frontier AI development, and facilitating the development of best practices.

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.

Federal prize competitions can help the U.S. government build a research and development ecosystem that incentivizes AI and cyber innovation and delivers for the American people. Over the last five years, prize competitions for AI and cyber innovation increased nearly 60%. When leveraged effectively, federal prize competitions offer unique benefits and can advance knowledge within a particular field or solicit solutions for specific government problems.

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.

Unlike other domains of conflict, and unlike other fields with high anticipated risk from AI, the cyber domain is intrinsically digital with a tight feedback loop between AI training and cyber application. Cyber may have some of the largest and earliest impacts from AI, so it is important to understand how the cyber domain may change as AI continues to advance. Our approach reviewed the literature, collecting nine arguments that have been proposed for offensive advantage in cyber conflict and nine proposed arguments for defensive advantage.

Despite recent upheaval in the AI policy landscape, AI evaluations—including AI red-teaming—will remain fundamental to understanding and governing the usage of AI systems and their impact on society. This blog post draws from a December 2024 CSET workshop on AI testing to outline challenges associated with improving red-teaming and suggest recommendations on how to address those challenges.