Implications of Large Language Models for OSINT: Assessing the Impact on Information Acquisition and Analyst Expertise in Prompt Engineering

Abstract

This paper explores the potential use of large language models (LLMs) in Open Source Intelligence (OSINT), with a focus on integrating information acquisition and the increasing importance of prompt engineering for analysts. The research includes a comprehensive literature review, which highlights the widespread use of AI in OSINT and the related challenges, such as data validity and ethical concerns. The study emphasizes the significance of prompt engineering as a crucial skill that demands a profound comprehension of LLMs to generate validated intelligence. A model of the OSINT lifecycle that incorporates LLMs is proposed. The paper further discusses updated training in critical thinking, search techniques, and prompt engineering for intelligence professionals. The findings indicate a noteworthy shift in OSINT procedures, highlighting the importance of continuous research and education to fully utilize AI in intelligence gathering.