CSET Senior Fellow Andrew Lohn testified before the House of Representatives Homeland Security Subcommittee on Cybersecurity, Infrastructure Protection, and Innovation at a hearing on "Securing the Future: Harnessing the Potential of Emerging Technologies While Mitigating Security Risks." Lohn discussed the application of AI systems in cybersecurity and AI’s vulnerabilities.

CSET Senior Fellow Andrew Lohn testified before the House of Representatives Science, Space and Technology Subcommittee on Investigations and Oversight and Subcommittee on Research and Technology at a hearing on "Securing the Digital Commons: Open-Source Software Cybersecurity." Lohn discussed how the United States can maximize sharing within the artificial intelligence community while reducing risks to the AI supply chain.

CSET Senior Fellow Andrew Lohn testified before the U.S. Senate Armed Services Subcommittee on Cybersecurity hearing on artificial intelligence applications to operations in cyberspace. Lohn discussed AI's capabilities and vulnerabilities in cyber defenses and offenses.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the third in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

Like traditional software, vulnerabilities in machine learning software can lead to sabotage or information leakages. Also like traditional software, sharing information about vulnerabilities helps defenders protect their systems and helps attackers exploit them. This brief examines some of the key differences between vulnerabilities in traditional and machine learning systems and how those differences can affect the vulnerability disclosure and remediation processes.

CSET submitted this comment to the Office of Science and Technology Policy on updating the National Artificial Intelligence Research and Development Strategic Plan.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the second in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

CSET Research Analyst Dakota Cary testified before the U.S.-China Economic and Security Review Commission hearing on "China’s Cyber Capabilities: Warfare, Espionage, and Implications for the United States." Cary discussed the cooperative relationship between Chinese universities and China’s military and intelligence services to develop talent with the capabilities to perform state-sponsored cyberespionage operations.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the first in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

​​This Classifying AI Systems Interactive presents several AI system classification frameworks developed to distill AI systems into concise, comparable and policy-relevant dimensions. It provides key takeaways and framework-specific results from CSET’s analysis of more than 1,800 system classifications done by survey respondents using the frameworks. You can explore the frameworks and example AI systems used in the survey, and even take the survey.