Datasets for Modeling and Mitigating Insider Risk Final Report and Codebook
Abstract
The breadth and scope of different variables that surround or are integral to understanding the risks that insiders may pose creates a complexity that requires additional applied and theoretical research across a wide range of topics and disciplines. Though data availability and privacy issues remain central issues in the field, data valuable for the study of insider threats, insider risk, detection, and mitigation do exist. The seedling project "Datasets for Modeling and Mitigating Insider Risk" (D-MInR) was designed specifically to identify extant datasets that may be useful for future Insider Risk efforts for the US government (USG) and its allies and partners, and to characterize those datasets in terms of their contents, location, relevance, accessibility, and other key parameters. D-MInR is the first known effort to track, catalogue, or characterize these potential resources across a wide variety of attributes and within multiple disciplines.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 2021
- Accession Number
- AD1185397
Entities
People
- Kathryn A. Lindquist
- Steve S. Sin
Organizations
- Art Libraries Society