Application of Artificial Intelligence/Machine Learning in Entity (Person of Interest) Scoring (Risk Profiling) for National Security

Abstract

In recent years, the imperative to maintain national security has led to the evolution of risk assessment methodologies, particularly the risk scoring of entities, encompassing both individuals and organizations. This paper delves into the intricacies of "Entity Risk Scoring" within the context of national security, examining its historical roots, modern methodologies, data sources, and associated ethical concerns. With the advent of machine learning and artificial intelligence, contemporary risk scoring has transcended traditional models, promising more holistic, data-driven insights. The utilization of diversified data sources, such as Open Source Intelligence (OSINT) and financial records, has further enhanced the accuracy and comprehensiveness of entity profiles. This paper discusses the enhancement of risk scoring for persons of interest through the application of AI. While the adoption of AI-driven techniques promises increased accuracy, it's essential to apply them with a clear emphasis on transparency, accountability, and the protection of basic human rights. The overarching objective is to fortify national security without impinging on the core values inherent to democratic societies.]
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