“AI red-teaming” is currently a hot topic, but what does it actually mean? This blog post explains the term’s cybersecurity origins, why AI red-teaming should incorporate cybersecurity practices, and how its evolving definition and sometimes inconsistent usage can be misleading for policymakers interested in exploring testing requirements for AI systems.

The EU AI Act is nearing formal adoption and implementation. Read this blog post, with updated analysis following the December 2023 political agreement, by CSET’s resident EU expert and Research Fellow, Mia Hoffmann. Learn what we know about the Act and what it means for AI regulation in the EU (and the world).

On September 8, 2023, Senators Richard Blumenthal (D-CT) and Josh Hawley (R-MO) released their Bipartisan Framework on AI Legislation. The framework includes many ideas and recommendations that CSET research has highlighted over the past four years. This blog post highlights some of the most relevant reports and CSET’s perspective on the framework’s elements.

As policymakers decide how best to regulate AI, they first need to grasp the different types of harm that various AI applications might cause at the individual, national, and even societal levels. To better understand AI harm, this blog post presents some key components and characteristics.

On July 21, the White House announced voluntary commitments from seven AI firms to ensure safe, secure, and transparent AI. CSET’s research provides important context to this discussion.

With the rapid integration of AI into our daily lives, we must all learn when and whether to trust the technology, understand its capabilities and limitations, and adapt as these systems — and our functional relationships with them — evolve.

Recent discussions of AI have focused on safety, reliability, and other risks. Lost in this debate is the real need to secure AI against malicious actors. This blog post applies lessons from traditional cybersecurity to emerging AI-model risks.

CSET's AI Assessment team provides a template that helps organizations create profiles to guide the management and deployment of AI systems in line with NIST's AI Risk Management Framework.

The goal of this guide is to acquaint researchers and analysts with tools, resources, and best practices to ensure security when collecting or accessing open-source information.