September 17, 2026
Yanliang Pan
The following is an excerpt from The Nonproliferation Review.
This report provides a survey of how machine learning (ML) could dramatically augment the speed, scale, and accessibility of major open-source-intelligence (OSINT) workflows for monitoring nuclear and missile proliferation. These tasks include data aggregation, anomaly detection, facial and object recognition, and satellite-imagery analysis. ML-enabled democratization of OSINT capabilities may well lower expertise barriers, enabling more robust nongovernmental analysis of weapons-of-mass-destruction threats. This could be instrumental in holding claims by intelligence agencies to account. However, without community-wide standards for ML adoption, overreliance on ML tools and unmitigated ML bias could undermine OSINT’s integrity. The erosion of data and methodological transparency could also degrade OSINT’s utility. Furthermore, hyper-transparency resulting from ML-enhanced detection could carry implications for strategic stability.
Continue reading at The Nonproliferation Review.
