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Assessment of the State and Local Anti-Terrorism Training (SLATT) Program

NCJ Number
250418
Date Published
2016
Length
120 pages
Annotation
This report presents the findings and methodology of an assessment of the State and Local Anti-Terrorism Training (SLATT) Program, which was established by the U.S. Justice Department's Bureau of Justice Assistance (BJA) to provide specialized terrorism/extremism orientation, interdiction, investigation, and prevention training to State, local, and tribal law enforcement executives, command personnel, patrol officers, intelligence officers, investigators, analytical personnel, and prosecutors.
Abstract

The SLATT Program has four components: 1) the Institute for Intergovernmental Research (IIR), which acts as the technical service provider; 2) U.S. government agencies and organizations that assist with planning and coordination; 3) instructors who teach the training modules; and 4) a web-based educational component. To date, the program has provided instruction to about 146,252 practitioners. The focus of this assessment was on State and local law enforcement agencies. Although local and State law enforcement agencies and personnel appreciate strategic concerns about jihadi and domestic terrorism threats, they expressed greater concern about domestic extremist group, particularly anti-government and criminal "sovereign citizen" elements, who disproportionately target law enforcement authorities. Still, based on a survey of State, county, and municipal law enforcement agencies, they assessed the local threat from both jihadi and domestic terrorism to be moderate rather than severe; however, this view has not reduced State and local law enforcement's interest in counter-terrorism (CT) training. Suggestions for improving the SLATT Program focus on the SLATT planning and assessment process, SLATT trainings and workshops, and the SLATT website. 21 tables , 17 figures, a 59-item bibliography, and appended survey of workshop participants, a description of select SLATT program features, and cost and benefit model statistics

Date Published: January 1, 2016