Abstract
Introduction
Over the last year, the Russian invasion of Ukraine has brought the value of open-source intelligence (OSINT) – the structured collection and analysis of publicly available information to achieve a targeted investigative outcome – into sharp relief.
This article explores the uses of AI and data science for analysing open-source intelligence related to Russia’s war in Ukraine – and considers how future capabilities can be leveraged most effectively.
From investigators tracking the military build-up before the invasion was officially announced to ongoing efforts to map the conflict, uncover war crimes and identify hostile information operations online, OSINT has repeatedly demonstrated its utility.
At the tactical level, the Ukrainian military is reported to have used Instagram and TikTok content to locate Chechen forces. Online groups – both civilian activists and formalised organisations – have been tracking Russian Navy deployments using publicly available satellite imagery and estimating total Russian military hardware losses from images posted to social media.
Due to the vast amount of data available via public sources, OSINT collection and analysis has typically been a time- and resource-heavy process. However, increased use of data science and machine learning is substantially improving the efficiency and scale of such investigations. Crowd-sourced reporting of enemy troop movements, especially when combined with increasingly precise computer vision models and enhanced availability of commercial satellite imagery, has improved situational awareness and allowed for near real-time tracking of the conflict. Geographic information system (GIS) software has helped identify areas of Ukraine littered with unexploded ordinance to prioritise for de-mining. Other notable advances include the extraction of information from swathes of unstructured data such as text, the deployment of computer vision models on commercial satellite imagery to provide situational awareness, and the use of statistical modelling and simulations to forecast events under multiple scenarios.
Machine-augmented techniques have also radically altered how intelligence work is done within the information battlefield. In this context, advances in scaled data access, network science and analytical techniques have enabled OSINT researchers to trace the contours of malign influence campaigns more accurately and dynamically — including, crucially, when the principal focus of these campaigns is not Western audiences. This allows for more targeted, tailored and impact-driven interventions.