A METHODOLOGICAL APPROACH TO THE SELECTION OF OSINT TOOLS FOR INFORMATION SECURITY TASKS

Abstract

This paper proposes a methodological approach to selecting Open Source Intelligence (OSINT) tools for addressing information security tasks. The relevance of the study is determined by the large number of modern OSINT tools, their functional specialization, differences in the level of automation, availability, accuracy, integration capabilities, and the nature of the information sources they utilize. These factors complicate the informed selection of an appropriate tool for a specific task and increase the dependence of the results on the analyst’s experience. Based on an analysis of contemporary approaches to OSINT application, the main categories of tools are systematized, including tools for external attack surface assessment, information leak monitoring, domain and network infrastructure analysis, SOCMINT, GEOINT, metadata analysis, data visualization, and data correlation. The selection process is proposed to be formalized as a multi-criteria procedure consisting of the initial filtering of tools according to their relevance to a specific information security task, followed by their ranking based on an integral score. The proposed evaluation criteria include the level of automation, API availability, ease of use, accuracy and reliability of results, information retrieval speed, accessibility, data export capabilities, and integration with other systems. Each criterion is evaluated using a five-level scale, while the overall score is calculated using the weighted sum method, taking into account the individual priorities of the user. Based on the proposed approach, a prototype system was developed to support the input of a task, configuration of criterion weights, filtering of the knowledge base, calculation of integral scores, and generation of a ranking of recommended OSINT tools. The proposed approach can serve as a basis for further development of automated OSINT analytics support systems, particularly through the integration of up-to-date tool data, external service APIs, and more advanced multi-criteria evaluation methods.