Integrating quantitative and qualitative reasoning to mitigate threats
Abstract
Traditional threat assessment approaches used by insider threat hubs and law enforcement are primarily reactive, focusing on the response to an incident and collecting forensic data to support the investigation. More mature, proactive programmes aim to prevent or mitigate risks with specialised behavioural threat assessment analysts, who use multidisciplinary approaches to identify and manage at-risk individuals before an incident occurs. Threat assessment methods range from the use of individual, unstructured clinical judgments to formal qualitative models that provide structured guidelines, to predictive analytic models. This paper describes the aims and characteristics of qualitative and quantitative models to anticipate insider threats and discusses the integration of these traditionally divergent threat assessment methods, highlighting the importance of combining human judgment, data science and artificial intelligence (AI) to develop holistic risk management strategies. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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Author's Biography
Frank Greitzer is the Chief Behavioural Scientist for Cogility Software, where he supports development and deployment of advanced decision intelligence solutions including predictive models for insider risk management. Frank is also the owner and Principal Scientist of PsyberAnalytix, which he founded in 2012 to perform research and consulting in social/behavioural sciences and information security. Prior to this, he served as Chief Scientist in cognitive informatics at the US Department of Energy’s Pacific Northwest National Laboratory. In Frank’s early career, after earning a BSc degree in mathematics from Harvey Mudd College, Claremont CA, USA, a MA degree in psychology and a PhD in mathematical psychology from UCLA, Los Angeles CA, USA, he served as a research psychologist for the US Navy and a human factors and artificial intelligence (AI) researcher in the aerospace industry. Among Frank’s most notable recent achievements is the development of a comprehensive knowledge base of behavioural and technical insider risk indicators. His contributions to research and practice also include numerous journal articles, conference papers and invited talks.
