Title:
AI-enabled Behavioral Threat Detection: Integrating Machine Learning with Cyberpsychological Indicators of Targeted Violence

Authors:
Dr. Robb Shawe, Dr. Robert W. Clark, USA

Abstract:

The increasing prevalence of digital communication has created unprecedented opportunities for identifying behavioral indicators associated with targeted violence. While traditional threat assessment frameworks rely heavily on observable behaviors and investigative intelligence, emerging advances in artificial intelligence (AI) and machine learning offer new capabilities for analyzing large volumes of digital behavioral data. This article explores integrating cyberpsychological indicators with machine learning to enhance early detection of potential threats in online environments. Drawing upon interdisciplinary literature from behavioral threat assessment, cyberpsychology, and computational intelligence, the article proposes a conceptual framework for AI-enabled behavioral threat detection. The framework illustrates how machine learning models may assist analysts in identifying patterns of grievance formation, digital identity reinforcement, and behavioral leakage within online discourse. The analysis highlights both the operational potential and the ethical considerations associated with AI-supported threat-detection systems. The article concludes by outlining future research directions and policy implications for integrating computational analytics into behavioral threat assessment practices.

DOI: http://dx.doi.org/10.51505/ijaemr.2026.11216