TRACE: A Structured AI-Supported Model for Cultic Risk and National Security Threat Assessment

Understanding the structure, behavior, and risk level of organizations exhibiting cult characteristics is crucial for defense, public order, and national security system structures, policymakers, and researchers. The range of organizations that exhibit these characteristics ranges from non-violent ideological movements to extremely dangerous extremist groups. This paper aims to develop an initial, first stage framework for assessing cult and national security risks, by creating an innovative multidimensional model that integrates psychological, organizational, and security indicators to assess organizations that exhibit cult-like characteristics. This framework, at later stages, after solid and comprehensive trial and testing by both AI and human subjects, can be refined and perfected as well as adapted in order to suit the specific needs of various branches and departments concerned with public order and national security. The difference between this academic effort and previous ones on the subject, which have taken into account in fragmentary and disparate evaluation elements such as psychological manipulation or leadership dynamics, is that this tool incorporates and integrates all these levels into a quantitative and qualitative scoring system to assess the level of potential threat of an organization to national security. This framework provides a first-stage to-be-tested structured method of distinguishing between high-risk groups, such as ISIS and Hizb ut-Tahrir, and low-risk ideological movements, such as the LaRouche Movement and NXIVM, by assessing factors such as charismatic leadership, ideological rigidity, social control, financial dependency, and security threats. Moreover, by establishing a concrete framework for qualitative and quantitative analysis, the process of collecting and analyzing information is thus facilitated, in particular by clearly identifying the direction of collection, as we will see. The framework is applied to four distinct case studies: ISIS, a global terrorist network that supports violent jihad and seeks territorial control; Hizb ut-Tahrir, an Islamist organization that promotes a global caliphate without engaging in direct violence but encourages ideological radicalization; The LaRouche Movement, a nonviolent political organization known for its conspiracy theories and hierarchical structure; and NXIVM,a self-improvement group that developed into a system of exploitation. The results illustrate how religious structures exist in different areas, but they have very different levels of risk. ISIS is approaching the maximum threshold, confirming its status as an existential security threat, while Hizb ut-Tahrir represents a latent risk through ideological radicalization. On the other hand, the LaRouche Movement and NXIVM, despite their internal coercive mechanisms, remain non-violent entities with minimal security implications for national security. A significant advance in this study is the integration of artificial intelligence (AI). AI can systematically process large amounts of data and assign preliminary scores based on patterns, organizational behavior, and geopolitical impact. However, the final evaluation and ranking of an organization must remain a human-driven process, in which intelligence analysts critically evaluate the AI-generated ratings, validate the arguments behind the assigned scores, and ensure that ethical considerations are adhered to. This approach aims to ensure both efficiency and accuracy, while preventing the misclassification of non-violent groups. This study demonstrates the applicability of the framework to various organizations, providing national security structures with a refined tool to assess, classify, and monitor risks and threats, while ensuring attention to ethical concerns.