Predictive Behavioral Risk Intelligence: An AI Framework for Insider Threat Detection Based on Cognitive and Psychological Indicators

Abstract

Insider threats account for over 30% of cyber incidents and cost organizations an average of $11.45 million per breach in 2023. Traditional detection systems often fail to anticipate these threats due to their psychological subtlety and contextual complexity. This study introduces the Behavioral Risk Intelligence Model (BRIM), an AI-driven framework that integrates forensic cyberpsychology, machine learning, and behavioral ethics for predictive insider threat detection. Using non-invasive behavioral profiling, BRIM identifies cognitive risk indicators such as digital validation-seeking, identity confusion, and Dark Triad personality traits. A thematic synthesis of 65 peer-reviewed studies reveals strong correlations between insider threats and these indicators, including 68% with validation-seeking, 74% with Dark Triad traits, 52% with identity instability, and 89% with algorithmic reinforcement. The model incorporates the Validation Syndrome Diagnostic Triangle (VSDT) to detect latent intent and emotional drift. By reframing insider threats as developmental and algorithmically conditioned rather than security violations, BRIM offers a proactive, ethically grounded approach to risk mitigation. The study recommends deploying BRIM in AI-powered dashboards for high-risk sectors, emphasizing privacy compliance and ethical surveillance.