Using Open Source Intelligence Methods within Due Diligence

This thesis explores the use of Open Source Intelligence (OSINT) methods within the due diligence process — a key component in assessing risks during mergers, acquisitions, and other business transactions. The theoretical part introduces the foundations of OSINT, including its development, tools, and the legal and ethical considerations associated with its use. It also outlines the structure and purpose of due diligence, with attention to its various forms such as financial, legal, operational, and IT. The core of the thesis presents a practical case study demonstrating how OSINT techniques can support reputational due diligence, specifically by identifying politically exposed companies in the United States connected to Donald Trump. This was achieved through the analysis of publicly available data from LittleSis and LobbyView, processed using Python and visualized in the Neo4j graph database. The result is a proposed framework that can be used to enhance decision-making in reputational and regulatory risk assessments. The thesis demonstrates that OSINT can serve as a powerful supplement to traditional investigative and evaluation methods.