Open-Source Vetting for National Security: AI-Driven OSINT and Insider Threat Detection
In an era of escalating insider threats and state-sponsored tech espionage, U.S. national security agencies are turning to open-source intelligence (OSINT) and unclassified data to vet individuals and companies for hidden risks. Open-source vetting for national security leverages AI, machine learning, graph databases, and data fusion to connect the dots across public information, enhancing risk assessments and spotting insider threats before they wreak havoc. From cutting-edge AI tools that flag anomalies in a contractor’s background to graph-powered analytics exposing covert networks, these innovations are reshaping how the Department of Defense (DoD) and Intelligence Community (IC) safeguard America’s secrets. This in-depth analysis explores how open-source vetting works in practice—and why recent insider espionage cases in 2024 and 2025 underscore its urgency.