Signals in the Noise: Preventing Nuclear Proliferation with Machine Learning & Publicly Available Information

High-risk, illicit trade in nuclear materials, equipment, and technologies continue to undermine global nuclear non-proliferation efforts. This has been true for decades. But guess what? Even the most sophisticated actors now leave more footprints —and a new report explores a new way to trace them.

The Nuclear Threat Initiative (NTI) and the Center for Advanced Defense Studies (C4ADS) on Tuesday, January 12 at 12:30pm ET released Signals in the Noise: Preventing Nuclear Proliferation with Machine Learning and Publicly Available Information.

At this webinar, the team demonstrated how publicly available data, when combined with machine learning, creates new opportunities to see, find, and expose potentially dangerous proliferation activities.