Applying Generative AI to Enhance Nuclear Safeguards Information Analysis

Volume: 68, December 2026, pages 24 - 44

Authors:  Carlos Angorrilla-Bustamante^a, Maria-Louisa Wightman^a, Erik Wolfart^a, and Guido Renda^a

^aEuropean Commission Joint Research Centre, Italy
 

Abstract: The nuclear safeguards regime is facing a critical challenge of information overload, where the volume of data from inspections, state declarations, and open sources threatens to exceed finite analytical resources. Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) offer a transformative opportunity to address this issue by automating analytical support tasks over vast, unstructured datasets, such as retrieval, filtering, summarization, and preliminary relevance scoring; safeguards judgment and validation remain the responsibility of human experts. This paper provides a comprehensive review of the application of GenAI to nuclear safeguards. It examines the institutional frameworks for AI governance being developed by bodies like the IAEA and Euratom and situates these efforts
within the broader context of GenAI’s impact on Open Source Intelligence (OSINT). The paper explores two primary deployment models for AI, namely general-purpose ‘horizontal’ platforms and specialized ‘vertical’ applications, and discusses the strategic considerations for their implementation. Finally, it presents the results of two demonstrator projects: a Retrieval-Augmented Generation (RAG) system for question-answering over a specialized news database, and an LLM-based workflow for filtering and analyzing large volumes of social media data. The findings highlight the potential of GenAI to serve as an analyst’s “co-pilot”, enhancing the efficiency and effectiveness of safeguards information analysis while retaining human review and decision-making.
 

Keywords:
Large Language Models, nuclear safeguards, data analysis, information overload, open-source information, ESARDA

Reference guideline: Angorrilla-Bustamante, C., Wightman, M.L., Wolfart, E., Renda, G., (2026). Applying Generative AI to Enhance Nuclear Safeguards Information Analysis, ESARDA Bulletin - The International Journal of Nuclear Safeguards and Non-proliferation, 68, 24-44. https://doi.org /10.3011/ESARDA.IJNSNP.2026.4