Publicly Available Information (PAI), also known as Open Source Intelligence (OSINT), is an increasingly important to the work done by intelligence analysts. Because OSINT can be used to identify emerging trends, tips, and cues, it is well suited to aid analysts in generating the breadth of hypotheses needed to maintain analytic rigor. However, managing an evolving set of (potentially) interdependent hypotheses comprised of the vast OSINT data landscape is both unwieldy and challenging. Our research team, under the sponsorship of the Air Force Research Laboratory (AFRL), has developed the Sensemaking for OSINT eXploitation (SOX) tool to assist analysts in creating, branching, and managing OSINT-based hypotheses using a unique visual model of hypothesis and evidence management. SOX integrates directly with web-based OSINT sources, and includes a custom suite of capabilities that analyze social network trends, patterns of life, and geospatial information to collect, filter, analyze, and aggregate OSINT intelligence. The result is a web-based tool that helps analysts “follow the data,” manage and corroborate evidence, and collaborate with peers to reduce workload in the OSINT big-data environment. In this paper we will describe the SOX approach to OSINT hypothesis management and human/autonomy collaboration and detail feedback gathered from USAF intelligence analysts in a series of evaluation events hosted by AFRL.
Phil DiBona, Hillary Holloway, and Jorge Tierno, "Hypothesis management for open source intelligence (OSINT) with the Sensemaking for OSINT eXploitation (SOX) tool (Conference Presentation)," Proc. SPIE 10653, Next-Generation Analyst VI, 1065307 (Presented at SPIE Defense + Security: April 16, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2304031.5783301384001.
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