Concept of information flow analysis technology within OSINT operations in instant messaging systems

Abstract

This document presents the results of an empirical study on the efficiency of hyper-automated information systems in processing large volumes of data during Open Source Intelligence (OSINT) operations. The focus is on the development and experimental validation of a conceptual architecture for a next-generation OSINT technology based on integration: Robotic Process Automation (RPA), machine learning tools, and large language models (LLM, particularly LLAMA 3). The technology is applied to data collection from instant messaging systems. The key stages of the hyper-automation pipeline are described: collection, preprocessing, analysis, and classification of data from Telegram channels. RPA bots were used for automated news extraction, followed by linguistic model processing to generate informative reports.
The experiment demonstrated a significant reduction in data processing time (from 6 hours to 17 minutes) while maintaining high accuracy (98%). The results were compared with manual processing and the potential for the proposed approach in information security and disinformation detection was evaluated. The findings indicate the strong potential of hyper-automation in intelligent information processing and data-driven decision-making.