Composite Signatures Analyst Learning Tool (CSALT): Supporting the Analyst with Scenario-Based Methodology Training

Abstract

ABSTRACTAnalysts across the intelligence community (IC) face rapid growth in the volume of data to be transformed into intelligence. Dramatic growth in open source intelligence (OSINT) is being driven by digital news outlets, blogs, and social media, particularly in conflict zones that lack traditional infrastructure and communications networks. At the same time, the IC is experiencing a reduction of skilled senior analysts due to attrition and budget contraction. At a time when all-source, multi-int, and open-source analysis are critical to combating terrorism, transnational threats, and hostile proto-states, skills and knowledge gaps must be aggressively remedied. A systemic improvement that could address these challenges is an approach to training that (a) embeds the effective use of proven, replicable methods for intelligence analysis in authentic scenarios, and (b) applies approaches to experiential, scenario-based learning that have been shown to yield effective outcomes. In this article, we describe a simulation enabled by online, scenario-based training incorporating a repeatable and proven analytic workflow. Composite Signatures Analyst Learning Tool (CSALT) applies scenario-based, technology-enriched training to create web-accessible interactive training scenarios. CSALT will equip analysts with the skills and knowledge to identify adversary locations, clandestine facilities, illicit activities, and other phenomenology using a proven composite signatures methodology.