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.
Intelligence Analysis remains a manual process despite trends toward autonomy in information processing. Analysts need agile decision-‐support tools that can adapt to the evolving information needs of the mission, allowing the analyst to pose novel analytic questions. Our research enables the analysts to only provide a constrained English specification of what the intelligence product should be. Using HTN planning, the autonomy discovers, decides, and generates a workflow of algorithms to create the intelligence product. Therefore, the analyst can quickly and naturally communicate to the autonomy what information product is needed, rather than how to create it.
Lockheed Martin Advanced Technology Laboratories (LM ATL) is researching methods, representations, and processes for human/autonomy collaboration to scale analysis and hypotheses substantiation for intelligence analysts. This research establishes a machinereadable hypothesis representation that is commonsensical to the human analyst. The representation unifies context between the human and computer, enabling autonomy in the form of analytic software, to support the analyst through proactively acquiring, assessing, and organizing high-value information that is needed to inform and substantiate hypotheses.
Lockheed Martin Advanced Technology Laboratories (LM ATL) is collaborating with
Professor James Llinas, Ph.D., of the Center for Multisource Information Fusion at
the University at Buffalo (State of NY), researching concepts for a mixed-initiative
associate system for intelligence analysts to facilitate reduced analysis and decision
times while proactively discovering and presenting relevant information based on
the analyst’s needs, current tasks and cognitive state. Today’s exploitation and
analysis systems have largely been designed for a specific sensor, data type, and
operational context, leading to difficulty in directly supporting the analyst’s evolving
tasking and work product development preferences across complex Operational
Environments. Our interactions with analysts illuminate the need to impact the
information fusion, exploitation, and analysis capabilities in a variety of ways,
including understanding data options, algorithm composition, hypothesis validation,
and work product development. Composable Analytic Systems, an analyst-driven
system that increases flexibility and capability to effectively utilize Multi-INT fusion
and analytics tailored to the analyst’s mission needs, holds promise to addresses the
current and future intelligence analysis needs, as US forces engage threats in
contested and denied environments.