Data Processing Cheat Codes for OSINT at Scale

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Data collection operations are dynamic and difficult, as sources and pivots through those sources are constantly changing. Achieving the greatest return on investment (ROI) of this data is critical for producing intelligence from OSINT. OSINT is hard to scale for a variety of reasons, as there isn't a standard schema, which is further exacerbated by the folks who gather the required data, as they aren't always data science-oriented individuals. Additionally, there is a lot of cross-domain expertise needed to turn an OSINT requirement into an analysis product. One such choke point is the Processing and Evaluation phase of the Intelligence Cycle referenced in DoD Joint Publication 2-0, Joint Intelligence. Factors that complicate this phase include data scale, diversity, and consistent processing methodology. This presentation suggests some methods and techniques to overcome some of the processing hurdles, from both a tactical and strategic data processing level.

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SANS Open-Source Intelligence Summit 2025
Data Processing Cheat Codes for OSINT at Scale
Matt Gorman, VP, Engineering, Prae, LLC