Designing, analysis, and evaluating OSINT software architectures through scenario-based methods: A QAW, ADD and ATAM approach

This paper proposes a novel software architecture designed specifically for Open-Source Intelligence (OSINT) systems, addressing critical challenges in performance, scalability, and security that arise from handling large volumes of diverse and dynamic open-source data. Leveraging established system-centric methodologies including the Quality Attribute Workshop (QAW), Attribute-Driven Design (ADD), and Architecture Trade-off Analysis Method (ATAM), this study systematically identifies and addresses architectural drivers, integrating them into an optimized OSINT architecture. A significant innovation of this architecture is the integration of an on-premise Large Language Model (LLM) within the data analysis layer, employing the Retrieval-Augmented Generation (RAG) architectural pattern to effectively process and analyse unstructured textual data. The architecture emphasizes scalability through distributed multi-process data acquisition systems, robust security protocols minimizing digital footprints, and advanced methods for semantic information retrieval and analysis. Empirical evaluations conducted through rigorous experiments involving multi-threaded and multi-process environments demonstrate the superior scalability, high availability, and fault tolerance of the proposed system. These results validate the hypothesis that distributed processing, combined with stringent access controls and on-premise data analysis solutions, substantially enhances operational performance and security. This research fills significant gaps in the existing literature by offering detailed conceptual and practical insights into the design and implementation of system-centric OSINT architectures, presenting a comprehensive framework applicable to cybersecurity, intelligence operations, and business intelligence sectors.