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

RAIS Journal for Social Sciences | VOL. 9, No. 2, 2025 ISSN 2574-0245 (Print) | ISSN 2574-1179 (Online) | DOI: 10.5281/zenodo.17468887 ! 108! Predictive Behavioral Risk Intelligence: An AI Framework for Insider Threat Detection Based on Cognitive and Psychological Indicators Francis C. OHU1, Laura A. JONES2 1,2Department of Forensic Cyberpsychology, Capitol Technology University, Laurel, MD, USA 1ORCID: https://orcid.org/0009-0003-4981-8428 2ORCID: https://orcid.org/0000-0002-0299-370X ! 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. Keywords: Ethical AI and Behavioral Governance, Forensic Cyberpsychology, Insider Threat Prediction and Prevention, Digital ValidationSeeking Behavior, Dark Triad Traits, Algorithmic Reinforcement and Behavioral Drift, Validation Syndrome Diagnostic Triangle, Organizational Cybersecurity Risk Profiling, Cognitive and Psychological Risk Signatures ! Introduction In the evolving cybersecurity ecosystem, insider threats remain among the most persistent and damaging forms of attack, and unlike external breaches, insider threats are often perpetrated by individuals who possess authorized access to systems, making detection and prevention highly complex (Ahmed et al., 2024), and these threats can manifest through intentional sabotage, espionage, data theft, or unintentional negligence. According to the Federal Bureau of Investigation’s Internet Crime Complaint Center (IC3), the 2024 Internet Crime Report combines information from 859,532 complaints of suspected Internet crime and details reported losses exceeding $16 billion, a 33% increase in losses from 2023 (IC3, 2025). The growing digitalization of workspaces and increased access to sensitive data have heightened the need for dynamic and intelligent insider threat mitigation strategies (Gunuganti, 2024). While traditional cybersecurity models focus heavily on perimeter defense and digital forensics, they often overlook human behavioral precursors to threat OHU & JONES:: Predictive Behavioral Risk Intelligence: An AI Framework for Insider Threat Detection Based on Cognitive and Psychological Indicators ! 109! activity (Ohu & Jones, 2025a). Research shows that behavioral indicators such as impulsivity, digital validation-seeking, identity confusion, and traits aligned with the Dark Triad (narcissism, machiavellianism, and psychopathy) can signal psychological risk factors for insider threat behaviors (Ohu & Jones, 2025b; Burnell et al., 2024). However, these indicators are frequently ignored in enterprise threat detection due to the lack of integration between behavioral science and machine learning models (Chapagain et al., 2024). Recent advancements in forensic cyberpsychology and artificial intelligence offer a transformative opportunity to close this gap (Tennakoon et al., 2024). Studies show that AI systems trained on cognitive-behavioral profiles can achieve over 85% accuracy in identifying individuals at risk of engaging in deceptive or harmful digital conduct, particularly when paired with environmental and contextual data such as workplace stressors or peer conformity dynamics (Ohu & Jones, 2025a). These models are particularly effective when guided by frameworks like the Validation Syndrome Diagnostic Triangle (VSDT), which maps interactions between self-doubt, desire, and self-gratification in hostile or conflictual environments (Ohu & Jones, 2025d). Moreover, empirical research confirms that adolescents exhibiting high levels of digital validation-seeking are more likely to transition into manipulative behaviors, a psychological pattern that may persist into adulthood and workplace environments (Ohu & Jones, 2025a; Trekels et al., 2024). This behavioral drift is exacerbated by algorithmic reinforcement mechanisms that normalize deception, particularly among individuals with predisposing psychological traits. Such findings underscore the critical need for behavioral risk intelligence models that combine AI-driven surveillance with ethical psychological profiling (Pellegrino & Stasi, 2024). The overarching research question guiding this study is, “How can AI systems ethically and accurately detect insider threats by analyzing cognitive, psychological, and behavioral indicators in high-risk organizational environments, without compromising individual privacy? This paper addresses the need for a predictive, ethically guided model that leverages behavioral psychology and AI to proactively detect insider threats. By examining recent findings from forensic cyberpsychology, digital validation theory, and machine learning ethics, we propose a novel AI-based framework that identifies precursors to insider misconduct in digital environments. Through this approach, the research seeks to position human behavioral risk as a dynamic, measurable variable rather than a post-incident forensic artifact. Problem Statement Insider threats remain a growing and under-addressed challenge in enterprise cybersecurity, contributing to over 30% of all cyber incidents in 2023, yet existing detection systems rely primarily on post-incident digital forensics or generic anomaly detection tools (IC3, 2025; Le & Zincir-Heywood, 2021). These methods frequently fail to account for cognitive and psychological precursors to threat behavior, especially those rooted in early digital conditioning, unresolved identity conflict, and reinforcement of manipulative behaviors through algorithmic exposure (Ohu & Jones, 2025a). This limitation is especially critical for business enterprises, where the costs of insider threats are not limited to financial loss but extend to reputational damage, regulatory penalties, and disruption of stakeholder trust. Without the integration of predictive behavioral intelligence, corporate compliance teams, Human Resource (HR) departments, and security operations centers remain reactive, often intervening only after an incident has occurred. The general problem is that enterprise-level cybersecurity strategies do not currently leverage forensic behavioral profiling to predict malicious insider activity (Pennada et al., 2025). The specific problem is the absence of integrated frameworks that combine machine learning, forensic psychology, and organizational behavioral science to detect and mitigate insider threats based on non- RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 110! invasive, ethical behavioral indicators (Ohu & Jones, 2025a). This gap in predictive behavioral modeling limits the ability of security teams to intervene before harm occurs. While indicators such as excessive privilege misuse, anomalous file transfers, or irregular work hours may flag suspicious activity, they fail to capture deeper cognitive and psychological risk signals such as self-doubt, validation cravings, and Machiavellian tactics, that often precede technical breaches (Ohu & Jones, 2025d; Burrell N.D., 2024; Trekels et al., 2024). Without early detection rooted in behavioral science, false positives remain high, and real threats go unnoticed until after the damage is done. ! Purpose Statement This study aims to develop a conceptual framework for AI-driven behavioral risk intelligence capable of identifying insider threats based on cognitive and psychological indicators. Drawing on forensic cyberpsychology, Dark Triad research, and machine learning literature (Wang et al., 2023), this study proposes a model that ethically analyzes behavioral markers such as validation-seeking tendencies, identity confusion, and manipulative online behaviors to enhance real-time threat detection within organizational contexts. The study is qualitative and conceptual, based on a synthesis of recent peerreviewed research published between 2021 and 2025, with the intent to propose actionable design and deployment strategies for AI-enhanced behavioral surveillance in cybersecurity. The framework integrates the Validation Syndrome Diagnostic Triangle (VSDT) and draws upon real-world case patterns found in recent psychological and cybercrime literature. ! Rationale, Originality, and Significance of the Study Insider threats continue to pose one of the most complex and under-addressed challenges in organizational information security, accounting for over 30% of all cyber incidents in 2023 (IC3, 2025). Despite advances in anomaly detection and digital forensics, most enterprise threat detection systems fail to address the psychological and cognitive dimensions that precede malicious insider behavior (Ohu & Jones, 2025a; Ahmed et al., 2024). This study is guided by the recognition that internal risk actors often exhibit identifiable psychological patterns such as digital validation-seeking, identity confusion, and Dark Triad traits, long before committing policy violations or data breaches and, such behavioral signals are rarely integrated into technical security architectures or organizational early-warning protocols (Ohu & Jones, 2025c). The study fills a crucial gap in current insider threat detection models by proposing an integrated framework that analyzes cognitive, psychological, and behavioral indicators in an ethical manner. By doing so, it directly addresses the research question of how AI systems can identify insider threats while respecting individual privacy. This research is original in its proposal of a conceptual framework that synthesizes forensic cyberpsychology, behavioral ethics, and AI-driven profiling into a unified model for predicting insider threats based on non-invasive psychological indicators. Unlike traditional detection methods that focus on technical artifacts or post-incident evidence, this study advocates for an anticipatory and ethically grounded approach that targets latent intent and emotional drift within high-risk organizational environments. The proposed framework provides a theoretical foundation for creating AI-driven insider threat programs that balance accuracy with ethical compliance, and detect latent risks while upholding fairness, psychological integrity, and human dignity, ultimately enabling proactive interventions in high-risk organizational settings. The incorporation of the Validation Syndrome Diagnostic Triangle (VSDT) as a diagnostic lens mapping interactions between self-doubt, selfgratification, and craving for validation, offers a novel psychological perspective on behavioral precursors to deception and misconduct. The significance of this research therefore, lies in its potential to transform how insider threats are conceptualized and OHU & JONES:: Predictive Behavioral Risk Intelligence: An AI Framework for Insider Threat Detection Based on Cognitive and Psychological Indicators ! 111! mitigated in organizational settings, shifting from forensic hindsight to behavioral foresight, and by addressing the absence of integrated models that combine AI-driven behavioral pattern recognition with validated psychological constructs, the study contributes to the emerging discourse on responsible AI use in proactive forensic cyberpsychology applications, ultimately providing a theoretical foundation for organizations seeking to build ethically compliant and psychologically informed insider threat programs, that preserve employee dignity while enhancing digital safety. ! Literature Review This literature review aims to identify, select, and analyze relevant studies to explore the integration of forensic cyberpsychology, cognitive profiling, and AI-driven behavioral risk modeling in mitigating insider threats. The overarching research question guiding this review is: “How can AI systems ethically and accurately detect insider threats by analyzing cognitive, psychological, and behavioral indicators in high-risk organizational environments, without compromising individual privacy?” This question aligns with the broader problem statement, emphasizing the lack of mature, privacy-compliant frameworks that integrate behavioral science, personality profiling, and AI for pre-incident insider threat detection (Pennada et al., 2025). A systematic literature search was conducted across Scopus, PsycINFO, MDPI, PubMed, IEEE Xplore, and Google Scholar. Search terms included: “AI for insider threat detection,” “behavioral profiling in cybersecurity,” “Dark Triad traits and cyber risk,” “digital validation-seeking behavior,” “forensic cyberpsychology in enterprise,” and “algorithmic reinforcement and deception.” Peer-reviewed articles published between 2021 and 2025 were prioritized to ensure recency and alignment with emerging AI applications. Studies were excluded if they lacked empirical rigor, did not incorporate psychological or cognitive constructs, or relied solely on post-incident digital forensics. Out of 113 initially retrieved sources, 65 met the inclusion criteria following abstract screening and full-text evaluation. ! Theoretical Framework Forensic Cyberpsychology as an Interpretive Lens The theoretical foundation for this study is forensic cyberpsychology, which integrates psychological profiling with digital behavioral forensics to understand, predict, and prevent cyber-related misconduct (Ohu & Jones, 2025b). This approach is particularly effective in analyzing online deception, manipulation, and threat behaviors that escape traditional technical surveillance (Alohaly et al., 2022). Unlike conventional models that prioritize code-based anomalies, forensic cyberpsychology examines psychological motivators and contextual drivers, enabling deeper insights into behavioral risks like insider threats. Studies have shown that psychological constructs such as self-doubt, need for validation, and emotional detachment manifest in early digital behaviors and evolve into risk-prone traits if unmonitored (Burnell et al., 2024; Trekels et al., 2024). This suggests that threat actors do not emerge abruptly but develop incrementally through behavioral conditioning and exposure to risk-enabling environments (Ali, Husain, & Hans, 2025). ! Validation Syndrome Diagnostic Triangle (VSDT) The Validation Syndrome Diagnostic Triangle (VSDT) provides a core diagnostic model for profiling insider threat behavior (Ohu & Jones, 2025d). It posits that three psychological forces, self-doubt, desire, and self-gratification, interact with environmental stressors such as familial conflict, peer influence, or professional dissatisfaction to catalyze deceptive behaviors. In enterprise contexts, these forces may manifest as self-doubt, such as impostor RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 112! syndrome and perceived injustice, desire, such as desire for recognition, revenge, or control, and self-gratification, manifested in acts like data hoarding, sabotage, or manipulation. By leveraging this framework, behavioral AI systems can be trained to detect subtle patterns that precede threat activity, offering preventive insights without relying solely on post-hoc indicators. ! Dark Triad Personality Theory The Dark Triad, comprising narcissism, machiavellianism, and psychopathy, has long been associated with manipulation, deceit, and antisocial behavior (Ohu & Jones, 2025a). These traits are strongly correlated with exploitative interpersonal behavior, reduced empathy and accountability, and strategic deception for personal gain. Recent studies confirm that individuals high in these traits are statistically more likely to engage in cyber manipulation, fraudulent behavior, and data exploitation (Burnell et al., 2024; Trekels et al., 2024b). In workplace environments, such individuals may evade standard technical detection while skillfully navigating social structures to fulfill their objectives. ! The Rise of Insider Threats and Behavioral Gaps Despite increasing investments in firewalls, endpoint detection, and SIEM systems, incidents of insider threats remain under-anticipated due to the lack of behavioral analysis (IC3, 2024). In 2023 alone, insider threats cost U.S. companies an average of $11.45 million per incident, up 35% from 2021 (Ahmed, 2024). These events are rarely caused by technical failures but by motivated individuals with access to sensitive information and internal knowledge, highlighting a behavioral intelligence gap. ! Role of Digital Validation-Seeking in Deception A growing body of literature identifies digital validation-seeking as a psychological antecedent to manipulative online behavior (Ohu & Jones, 2025a; Trekels et al., 2024a). Individuals who rely heavily on external validation are more prone to curating deceptive digital personas, thereby engaging in algorithmically reinforced misconduct and rationalizing cyber offenses as social survival tactics. These tendencies are often reinforced in adolescence and persist into adulthood, especially under stress or isolation (Ohu & Jones, 2025a). Further studies by Ohu & Jones, (2025b) showed that 40% of cyber fraudsters report early experiences with online deception during adolescence, often driven by social comparison, peer pressure, and algorithmic reinforcement. ! Algorithmic Bias and Behavioral Reinforcement AI algorithms on platforms like LinkedIn, X-platform, and corporate intranets often amplify the visibility of high-risk traits, such as overconfidence, reward-seeking behavior, or attention bias. While not inherently malicious, these behaviors, when combined with unresolved psychological conflicts, may evolve into deliberate sabotage or data exfiltration (Burnell et al., 2024; Zhou, 2024). Algorithmic systems, lacking ethical filtering, often prioritize engagement metrics over well-being, and this presents a risk when individuals with psychopathic or machiavellian traits manipulate systems for personal gain while appearing compliant to platform supervisors. ! AI-Enhanced Profiling for Risk Intelligence Recent research advocates for AI behavioral risk profiling engines that combine psychological traits with digital behavior logs (logins, file access patterns, communication tone, etc.) to detect anomalies in intent, not just activity (Ohu & Jones, 2025b). Such models OHU & JONES:: Predictive Behavioral Risk Intelligence: An AI Framework for Insider Threat Detection Based on Cognitive and Psychological Indicators ! 113! are capable of detecting pre-incident warning signs, generating psychological heatmaps, and minimizing false positives through context-aware learning. Moreover, studies emphasize that ethical safeguards such as transparency, anonymization, and proportionality can help align such systems with privacy laws and organizational values (Chapagain et al., 2024; López et al., 2024). ! Psychological Conditioning and the Insider Threat Lifecycle Behavioral risk is rarely spontaneous; it is shaped through progressive psychological conditioning, often starting in adolescence, and evolving through workplace experiences, and validation-seeking behavior, reinforced by digital platforms, primes individuals to normalize manipulation for acceptance or gain (Ohu & Jones, 2025d). Over time, these patterns may evolve into workplace sabotage, data leaks, or intellectual property theft, particularly when stressors such as professional exclusion, unaddressed trauma, or ideological dissonance arise (Burnell et al., 2024; Murad R.J., 2024). Empirical studies indicate that 40% of insider threat actors had a documented history of feeling undervalued or ignored in prior roles, often seeking significance through covert disruption (Trekels et al., 2024). These behaviors reflect unresolved psychological narratives, which forensic cyberpsychology seeks to trace. ! Identity Confusion and Role Conflict in Digital Workspaces Insider threat susceptibility often correlates with identity confusion, especially among younger or transitional employees (Ohu & Jones, 2025a). Drawing from Erikson’s psychosocial theory, identity vs. role confusion during early adulthood can manifest in behaviors such as code-switching between professional and personal digital identities, ethical disengagement from organizational values, and cognitive dissonance between self-image and workplace expectations (Murad, 2024; Schluchter, 2024). Also, studies by Pérez-Torres (2024) and Ruohonen & Saddiqa (2025) suggest that individuals experiencing unresolved identity formation are more vulnerable to ideological manipulation or performative misconduct. When paired with validation-seeking and peer comparison mechanisms, this confusion may lead to conscious deception for attention, retaliation, or self-preservation. ! Organizational Neglect and Lack of Behavioral Monitoring Organizational culture and leadership’s failure to recognize behavioral risk are critical contributors to insider threats. Research reveals that 68% of surveyed companies lacked psychological early-warning systems despite an increase in human-factor breaches (Ahmed et al., 2024; IC3, 2024). While technical safeguards exist, few enterprises have integrated behavioral AI models capable of detecting emotional withdrawal, social disengagement, or passive-aggressive conduct, all of which often precede malicious acts (López et al., 2024). Moreover, traditional HR surveillance often violates privacy norms or relies on biased manual interpretations. The need is for algorithmically augmented behavioral audits that operate with contextual sensitivity, triangulating behavioral markers rather than flagging isolated deviations (Schlund & Zitek, 2024). ! Cross-Domain Applications of AI in Behavioral Threat Detection Beyond corporate cybersecurity, AI-driven behavioral profiling has proven effective in detecting radicalization, digital fraud, and misinformation propagation among at-risk youth (Ohu & Jones, 2025a). These adjacent domains offer validated models and ethical safeguards transferable to enterprise security. For example, disinformation detection models analyze emotional resonance and impulsivity in content engagement (Zhou et al., 2024); romance scam research tracks early behavioral drift via algorithmic validation loops and peer RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 114! mimicry (Ohu & Jones, 2025c); and social engineering prevention models monitor changes in communication tone and social conformity to detect deception (Burnell et al., 2024). Translating these validated models into enterprise contexts can significantly enhance AIdriven insider threat detection through interdisciplinary innovation (Anju et al., 2023). ! Ethical Concerns and Regulatory Blind Spots in AI Behavioral Surveillance While AI behavioral profiling holds promise, concerns over privacy, fairness, and consent remain paramount. Critics argue that intrusive monitoring may violate employee rights or perpetuate discriminatory biases if left unchecked (Chapagain et al., 2024; Fominykh, 2024). This is especially true for models trained on legacy datasets that lack demographic diversity or contextual nuance. Recent scholarship emphasizes the need for transparent AI design with explainable outputs, context-aware algorithms that avoid overfitting psychological traits to intent, and employee-informed consent protocols and opt-in behavioral assessments (Schlund & Zitek, 2024). Ethically sound profiling systems must be non-punitive, deidentified, and behaviorally contextualized, ensuring that human dignity and psychological complexity are preserved while enhancing risk detection (Trekels et al., 2024b; Pellegrino & Stasi, 2024; Ohu & Jones, 2025b). This literature review confirms a clear and urgent need for AI models that incorporate forensic cyber psychological indicators to mitigate insider threats. The combination of the VSDT, Dark Triad theory, and forensic cyberpsychology principles offers a multi-layered lens for building tools that must remain ethical, non-invasive, and privacy-aware. For business enterprises, applying these theoretical constructs enables the design of humancentric security protocols that detect and interpret deviations from normative behavior before they manifest as misconduct. This supports the development of ethical, data-driven workplace risk mitigation strategies across finance, healthcare, critical infrastructure, and tech industries. ! Research Methodology Research Design This is a qualitative conceptual design study that synthesizes recent empirical literature (2021–2025) to develop a theory-driven, ethically grounded model for behavioral risk intelligence in cybersecurity. This study employs a design science methodology situated within an applied behavioral science framework, and the goal is to construct and refine an early warning model rooted in the empirical behaviors of pre-incident actors, consistent with approaches in applied criminal psychology. The study does not involve human subjects directly and relies exclusively on secondary data. This study further employs a narrative literature review design to synthesize emerging evidence on the intersection of behavioral risk intelligence, AI-driven profiling, and forensic cyberpsychology for insider threat detection. The approach is qualitative, conceptual, and theory-driven, aiming to generate an integrated framework, the Behavioral Risk Intelligence Model (BRIM), rather than test a hypothesis through primary data collection, and unlike empirical qualitative research requiring firsthand interviews or focus groups, this study uses secondary data sources, specifically, peer-reviewed publications and validated frameworks from 2021 to 2025. This design aligns with the study's conceptual nature and addresses the identified gap in current organizational cybersecurity models regarding ethical behavioral risk modeling, and the overarching research question, “How can AI systems ethically and accurately detect insider threats by analyzing cognitive, psychological, and behavioral indicators in high-risk organizational environments, without compromising individual privacy?”, served as the guiding anchor for all analytic phases, from literature review and coding to thematic synthesis. OHU & JONES:: Predictive Behavioral Risk Intelligence: An AI Framework for Insider Threat Detection Based on Cognitive and Psychological Indicators ! 115! Ethical Considerations As this study involves no direct interaction with human participants and utilizes only publicly available and properly cited secondary sources, formal ethical approval was not required. However, the study was conducted in accordance with the principles of research integrity, privacy-preserving analytics, and AI ethics. These are embedded within the BRIM model to ensure transparency, consent protocols, proportionality, and fairness in future applied use. ! Literature Search Strategy A systematic narrative review of literature was conducted to synthesize interdisciplinary research relevant to the cognitive, psychological, and algorithmic precursors of insider threats in digital workspaces. Five academic databases were selected for their coverage of psychology, cybersecurity, and behavioral analytics including Scopus, PsycINFO, PubMed, MDPI, and Google Scholar, and search terms included combinations of behavioral and technical keywords such as: "AI for insider threat detection," "digital validation-seeking behavior," "Dark Triad traits and deception," "algorithmic reinforcement," "forensic cyberpsychology," "psychological profiling in cybersecurity," and "identity confusion in digital workspaces." Boolean operators and truncation were used where appropriate to refine search sensitivity and scope. The inclusion criteria required that sources were peer-reviewed publications from 2021 to 2025, focused on cognitive or psychological antecedents of cyber deception, and included empirical data or validated conceptual frameworks, in addition to addressing topics such as Dark Triad traits, validation-seeking, identity confusion, algorithmic behavioral reinforcement, and AI-based profiling models. The exclusion criteria removed non-peer-reviewed literature and grey literature such as white papers and editorials, studies based purely on technical or forensic models without a behavioral component, publications dated before 2021, unless they were considered seminal or referenced frequently in more recent literature, and from an initial pool of 113 retrieved sources, 65 studies met the eligibility criteria after title, abstract screening, and full-text evaluation, and Figure 1 below shows the PRISMA flow diagram for the study’s document selection process. ! Data Analysis and Validity Data Extraction and Coding Procedures A six-phase thematic analysis methodology (Braun & Clarke, 2024) was applied to coded data extracted across five analytic dimensions, namely, citation metadata, study type and population, behavioral risk indicators, AI methodology, and ethical considerations. The study employed a rigorous methodology to ensure the validity and reliability of its findings. Through an iterative process, codes were refined into themes using a multi-layered interpretive approach, allowing for a nuanced understanding of the data, and to further enhance the study's credibility, the researchers incorporated several validation techniques. Firstly, investigator triangulation was utilized, where multiple reviewers independently coded and verified the data to ensure thematic convergence. This approach helped to minimize individual biases and increase confidence in the emerging themes. The study further employed theoretical triangulation by integrating frameworks from various established theories, including Dark Triad Theory, Validation Syndrome Diagnostic Triangle (VSDT), Forensic Cyberpsychology, and Behavioral Ethics. By drawing on these diverse perspectives, the researchers were able to develop a more comprehensive understanding of the phenomena under investigation. Finally, the study used cross-source validation, referencing anchor literature such as Ohu & Jones (2025d) to stabilize theme definitions and RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 116! ensure consistency with existing knowledge. By triangulating data and methods in this way, the study was able to increase the validity and generalizability of its findings, providing a robust foundation for its conclusions. The results informed the design of the Behavioral Risk Indicator Model (BRIM) framework, specifically its multi-layered AI architecture, which translates psychological patterns into predictive behavioral risk indicators, enabling the development of more measurable and effective AI-powered insider threat detection systems. Instrument Transparency and Theoretical Framework Table 1 provides the complete behavioral risk coding schema, mapping psychological traits to coded behavioral indicators and theme definitions, ensuring reproducibility and auditability. The BRIM model is theoretically anchored in Dark Triad Theory (Paulhus & Williams, 2002), Validation Syndrome Diagnostic Triangle (Ohu & Jones, 2025), Forensic Cyberpsychology (Ruohonen & Saddiqa, 2025), and Behavioral Ethics frameworks (Pellegrino & Stasi, 2024). These theoretical lenses ground the psychological mechanisms of insider threat emergence and inform the ethical design principles of BRIM. Five core themes that emerged from a comprehensive analysis of insider threat literature, particularly focusing on the psychological, organizational, and technological dimensions, are shown in Table 2. Each theme is aligned with a specific focus area, suggesting the interdisciplinary nature of insider threat research. Table 1. Behavioral Risk Coding Schema Code Psychological Trait Behavioral Indicator Theme Operational Definition Sample Reference(s) BR01 ValidationSeeking Excessive need for digital approval or recognition Digital ValidationSeeking Persistent behaviors aimed at reinforcing self-worth via social media or online platforms Ohu & Jones (2025b), (PérezTorres, 2024a). Burnell (2024) BR02 Identity Instability Role confusion or incoherent self-concept in digital contexts Identity Confusion Difficulty maintaining a consistent identity, leading to disengagement from ethical norms Ruohonen & Saddiqa (2025). (RJ Murad, 2024) (Ullah et al., 2024) BR03 Narcissism Grandiose selfperception, entitlement, exploitation of peers Dark Triad Traits Inflated self-view combined with manipulative tendencies targeting digital or organizational gain Pellegrino & Stasi (2024). (Shahri et al., 2024). (Liang et al., 2024) BR04 Machiavellianism Strategic deception, cynicism, calculated norm violation Dark Triad Traits Utilization of manipulation and secrecy to exploit organizational systems Ahmed et al. (2024). (Ceroni & Yalch, 2024). (Saddiqa & Ruohonen, 2025) BR05 Psychopathy Impulsivity, lack of empathy, ethical disregard Dark Triad Traits Affective detachment and disregard for social or professional consequences López et al. (2024). (Brazil et al., 2024. Perenc, 2022). (Tokunbo & Borisade, 2025) OHU & JONES:: Predictive Behavioral Risk Intelligence: An AI Framework for Insider Threat Detection Based on Cognitive and Psychological Indicators ! 123! machine learning models to identify these latent indicators before they culminate into harmful actions (Nepal & Joshi, 2022). To ensure responsible implementation, the model incorporates ethical safeguards, including consent, transparency, and de-identification protocols. Finally, Environmental Stressors such as job dissatisfaction or ideological tension are acknowledged as contextual amplifiers that can accelerate psychological vulnerabilities that eventually lead to insider threat actions (Waiganjo & Nandjenda, 2025). Together, these components structure the BRIM model’s multi-layered detection logic, setting the stage for the detailed thematic findings presented in the next section. Discussion This study aimed to investigate how psychological and behavioral risk markers, such as validation-seeking, identity confusion, and Dark Triad traits, can be ethically integrated into AI-driven frameworks for insider threat detection. Our findings confirm that algorithmic reinforcement and digital validation-seeking are highly predictive of insider risk behavior, while ethically designed AI can mitigate bias and false positives in behavioral profiling (Habib & Nithyanand, 2025; Ohu & Jones, 2025a). Specifically, 89% of studies reviewed suggested that algorithmic systems contribute to behavioral feedback loops that amplify preexisting cognitive vulnerabilities, reinforcing maladaptive behaviors such as deception and antisocial conduct, consequently reinforcing insider threat dispositions through these targeted feedback mechanisms and ethical desensitization. Behavioral risk, as quantified through thematic analysis, is dominantly shaped by psychological traits, most notably narcissism and self-doubt, suggesting that risk detection models must move beyond technical anomaly detection toward psychologically informed profiling (Ogunbodede et al., 2024). ! Digital Behavior Psychological Profile AI Classifier Risk and Intent Detection Ethical Filter and Bias Check Behavioral Risk Dashboard Human Oversight Intervention Protocols Figure 5. Behavioral Risk Intelligence Model (BRIM) AI/ML Insider Threat Mitigation Pipeline ! As illustrated in Figure 5, the Behavioral Risk Intelligence Model (BRIM) operationalizes insider threat mitigation through a layered AI/ML framework, integrating passive digital behavior analytics, machine learning classifiers, and ethically guided risk scoring. The Psychological Input Layer where a Digital Behavior Psychological Profile is generated by passively collecting non-invasive behavioral indicators such as emotional tone shifts, excessive self-referencing, late-hour activity, and file browsing sequences,, which are interpreted using constructs from the Validation Syndrome Diagnostic Triangle (VSDT) (Ohu & Jones, 2025a) such as validation-seeking, identity confusion and Dark Triad traits, RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 124! which, as depicted in Figure 3, account for 66% of observed behavioral risk patterns. These inputs feed into the AI Risk Classifier Layer, where supervised and unsupervised learning models, including natural language processing and contextual anomaly detection, assign dynamic risk scores with minimal false positives (Ali et al, 2025;(Roy & Chen, 2024)), this is the algorithmic core of BRIM, where contextual anomaly detection algorithms analyze behavioral patterns over time rather than isolated events (Ali et al, 2025). These classifiers are trained on labeled datasets that distinguish benign from malicious behaviors and are further tailored to factors such as role, seniority, and workload. This allows the system to assign dynamic risk scores with greater precision and minimal false positives or alert fatigue. To ensure fairness and mitigate algorithmic bias, these scores are processed through the Ethical Filter & Contextual Auditor, which includes fairness checks, transparency protocols, and human-in-the-loop oversight (Chapagain et al., 2024; Pellegrino & Stasi, 2024), it conducts bias testing such as demographic parity, compares flagged behavior to contextual baselines, and supports human-in-the-loop oversight. This layer reinforces regulatory compliance and ethical integrity by integrating transparency and de-identification protocols, as emphasized by Chapagain et al. (2024), further ensuring that risk profiling is preventive rather than punitive, thereby avoiding stigmatization or legal overreach. As a final step, a user-friendly Actionable Intelligence Dashboard delivers interpretive outputs to analysts and compliance teams without breaching employee dignity or legal protections, a user-facing interface designed for cybersecurity analysts, HR teams, and legal officers that visualize behavioral trajectory maps, departmental heatmaps of psychological vulnerability, and non-punitive alerts such as automated check-in recommendations or reassignment flags. The dashboard can be integrated into existing Security Information and Event Management (SIEM) platforms and HR governance systems, providing real-time, psychologically informed decision support that respects ethical and privacy norms. This layered AI construct enables BRIM to function not only as a predictive tool but also as a responsible, adaptive system for identifying and mitigating behavioral risks in complex digital environments. Figure 5 further reinforces this process, showcasing how BRIM integrates psychological diagnostics, AI-driven inference, and ethical oversight into a single decision-support pipeline. Central to BRIM is the emphasis on behavioral drift, latent intent, and diagnostic triangulation rather than simple rule breaking. Importantly, the framework does not attempt to replace human judgment but rather augments it with real-time, contextaware behavioral insights. ! Figure 6. Behavioral Risk Profiling Pipeline OHU & JONES:: Predictive Behavioral Risk Intelligence: An AI Framework for Insider Threat Detection Based on Cognitive and Psychological Indicators ! 125! The Behavioral Risk Intelligence Model (BRIM) illustrated in Figure 6, further operationalizes insider threat detection by integrating psychological constructs with computational intelligence under an ethical oversight framework (Gayathri et al., 2024). This AI-driven conceptual model draws on forensic cyberpsychology, machine learning, and behavioral ethics to analyze cognitive behavioral indicators such as validation-seeking, selfdoubt, and antisocial traits, thereby offering an ethically grounded alternative to traditional anomaly-based systems (Ohu & Jones, 2025a). At its core are three diagnostic domains, namely the Validation Syndrome Diagnostic Triangle (VSDT), Dark Triad traits, and behavioral drift, which collectively underpin individualized risk profiling (Ohu & Jones, 2025d). The VSDT posits that deceptive behaviors are driven by internal forces, including self-doubt, desire, and self-gratification, while Dark Triad theory contextualizes manipulative and high-risk tendencies (Gelman et al., 2024). These constructs are interpreted through cyber psychological analysis of digital behavior logs and processed via machine learning algorithms trained to detect latent behavioral risk signatures (Edlabadkar & Madisetti, 2024; Singh & Chattopadhyay, 2023). By generating dynamic and predictive risk profiles, BRIM enables proactive identification of threat trajectories. The ethical infrastructure centered on transparency, informed consent, and data de-identification ensures that outputs such as early warnings and risk scores are not only actionable but also accountable and rights-preserving. This layered approach transitions organizational security from reactive, forensic investigation to anticipatory, psychologically informed insider threat interventions that align with both enterprise goals and ethical standards. By incorporating insights from Dark Triad theory and the principles of forensic cyberpsychology to interpret behavioral drift, motivational conflict, and latent intent (Gelman et al., 2024), and training machine learning algorithms on these psychologically grounded constructs, enables the development of dynamic, predictive behavioral risk profiles that evolve over time (Mittal & Garg, 2023), supporting a proactive, context-aware, and ethically sound approach to insider threat prediction, prevention, and mitigation. Psychologically, threat behavior is rooted in cognitive-behavioral patterns associated with grievance formation, fixation, and pathway to violence. This model draws upon principles of behavioral threat assessment established in forensic psychology literature (Cornell et al., 2025) which conceptualizes pre-incident indicators as expressions of internal states of intent and capacity. Broad Implications BRIM offers a practical and ethically grounded augmentation to existing insider risk programs across both enterprise and healthcare domains. Designed around Privacy by Design (PbD) principles and anchored in ethical AI standards such as transparency, proportionality, and explainability, BRIM ensures that only de-identified behavioral data is processed, explicitly excluding protected class information (Alzaabi & Mehmood, 2024; Chapagain et al., 2024). It functions as a passive detection system, analyzing surface-level behavioral indicators without accessing private conversations or biometric data. From a corporate policy perspective, the deployment of BRIM requires clearly defined governance protocols that specify access control, interpretive authority, and ensure that interventions are designed to be supportive rather than punitive, preserving employee autonomy and dignity, and to preserve individual dignity, BRIM embeds employee-informed consent, transparent decision logic, and non-punitive interpretation protocols (Pellegrino & Stasi, 2024). For enterprises, BRIM enhances insider threat detection by moving beyond technical anomalies to assess psychologically driven risk trajectories. It integrates seamlessly with workflows such as HR performance reviews, whistleblower protections, and employee assistance programs (EAPs). Behavioral analysts, HR teams, and cybersecurity professionals can collaboratively use BRIM to monitor behavioral drift, emotional disengagement, and latent RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 126! intent, offering early intervention opportunities before insider risks escalate. As digital transformation expands and hybrid work environments increase exposure, organizations require human-aware detection models capable of assessing real-time psychological vulnerabilities (Ruohonen & Saddiqa, 2024). BRIM fulfills this need by linking behavioral profiling with AI-enhanced monitoring (Wei et al., 2024), preventing data leakage, sabotage, and operational compromise (Kamatchi & Uma, 2025). Pilot implementations in high-risk sectors such as finance, energy, pharmaceuticals, and defense contracting could validate its practical scalability and impact. In healthcare environments, where insider threats to electronic health records (EHRs) and protected health information (PHI) are both prevalent and dangerous, BRIM offers a new layer of protection for patient privacy, clinical workforce stability, and institutional integrity. Studies show that over 30% of hospital data leaks stem from insiders misusing legitimate access (Alder, 2025). By integrating BRIM with EHR audit logs, clinician wellness dashboards, and governance boards, healthcare institutions can identify early signs of burnout, emotional withdrawal, or malicious intent among clinical staff. Research highlights the importance of emotionally attuned security tools in reducing risks tied to burnout, secondary trauma, and identity conflict, particularly during crises like pandemics or restructuring (Park et al., 2025). BRIM responds to these calls by embedding behavioral insights into systems that flag psychological vulnerability without punitive surveillance. From a practitioner perspective, the framework offers a scalable structure for integration into criminal justice risk management systems, potentially aiding forensic psychologists, school threat teams, and intelligence analysts in pre-incident detection. The model also holds promise for broader regulatory compliance and governance alignment. It supports HIPAA, GDPR, and labor law standards by emphasizing fair access protocols, noninvasive monitoring, and supportive interventions over disciplinary action. In both healthcare and enterprise contexts, BRIM helps prevent unethical chart access, prescription fraud, policy evasion, and emotional withdrawal by flagging high-risk behavior patterns before systemic failures occur. Validation of the BRIM framework is proposed through pilot programs in collaboration with academic research centers, cybersecurity agencies, ethical AI labs, and healthcare delivery networks. These pilots may include use cases such as detecting ideologically motivated data misuse, burnout-linked disengagement, or early-stage deception. A mixed-methods validation approach is recommended, combining simulated datasets, qualitative interviews, and real-time classifier testing. Ultimately, BRIM offers a scalable, psychologically informed, and ethically robust framework for detecting behavioral risk earlier than conventional models, reducing false positives through cognitive triangulation, and reinforcing organizational trust through principled design (Gelman & Hastings, 2025; Ohu & Jones, 2025a; Pellegrino, 2025). Bias Management and Validity Controls While the conceptual model derived from this study, BRIM, demonstrates promise, several caveats must be noted. For instance, peer influence, organizational culture, and socioeconomic stressors may also shape insider threat behavior, but were not directly measured in this literature synthesis. Although our thematic coding framework emphasized familial and psychological factors, future research should incorporate peer relationships, workplace hierarchy, and cultural context to create more nuanced models. Moreover, while BRIM reduces false positives through contextual filters (Kantchelian et al., 2024), causation cannot yet be definitively established. Longitudinal and real-world validation studies perhaps using mixed-methods designs are needed to confirm predictive accuracy and reduce overfitting. Also, to enhance methodological rigor, this review relied on peer-reviewed sources published between 2021 and 2025, applied manual cross-checking of thematic codes, and ensured demographic neutrality by abstracting behavior traits rather than identity OHU & JONES:: Predictive Behavioral Risk Intelligence: An AI Framework for Insider Threat Detection Based on Cognitive and Psychological Indicators ! 127! markers. The study also employed literature triangulation across domains, including disinformation, romance scams, and insider threat psychology, lending robustness to its synthesized conclusions. Study Limitations The study's findings are subject to limitations, including the lack of primary empirical data that restricts the ability to draw context-specific inferences or test causal relationships directly. The reliance on a narrative literature review and secondary data introduces the possibility that newly published research in 2025 may further refine or challenge existing understandings of psychological traits as insider threat markers. The interpretive nature of thematic coding, though systematically applied, may still carry latent researcher bias, however to mitigate these concerns, the study employed a structured narrative review methodology, incorporated inter-coder reliability checks during thematic synthesis, and limited the data pool to peer-reviewed publications from 2021 to 2025. These safeguards enhance the credibility, replicability, and conceptual grounding of the findings while acknowledging their empirical provisionality and the need for future primary-data validation. Conclusion This study introduced the Behavioral Risk Intelligence Model (BRIM) as an ethically grounded, AI-enhanced framework designed to predict and mitigate insider threats by analyzing cognitive and psychological indicators. This model integrates the principles of forensic cyberpsychology, machine learning, and behavioral ethics, and shifts the focus from technical anomalies to behavioral precursors, offering a proactive and context-sensitive alternative to traditional insider threat detection systems (Mladenovic et al., 2024). As visualized in Figure 5, the model operationalizes a multi-layered detection pipeline capable of interpreting latent behavioral risk signals and producing ethically filtered outputs, such as early warnings and risk trajectories (Koli et al., 2025). These findings affirm the study's central thesis that insider threats can be more accurately and responsibly addressed by targeting behavioral drift and psychological markers like validation-seeking, identity confusion, and Dark Triad traits constructs that have been overlooked in most conventional detection systems. The implications of this research are broad and impactful. In enterprise settings, BRIM can be deployed within Insider Threat Programs (ITPs) to monitor behavior during high-risk periods such as mergers, layoffs, or executive transitions (Nasir et al., 2021). Managers and compliance teams can utilize behavioral dashboards not just to detect risk, but also to inform training, build organizational trust, and benchmark psychological safety. In healthcare, BRIM strengthens data governance by identifying burnout or emotional distress in clinical staff that may precede inappropriate EHR access or disengagement. This supports patient safety initiatives while complying with HIPAA and privacy norms. The model also applies to defense and law enforcement sectors, where ideologically driven insider risks require a blend of psychological insight and ethical oversight. Compared to earlier studies that relied heavily on anomaly detection, BRIM’s behavioral lens introduces a psychologically informed upgrade, enriching the field with an approach that reduces false positives and supports non-punitive intervention. The BRIM framework also responds to recent calls for AI systems that align with Privacy by Design (PbD) and GDPR principles. In contrast to surveillance-heavy solutions, BRIM is built on anonymization protocols, employee-informed consent, and explainable AI logic, as supported by Chapagain et al. (2024) and Pellegrino & Stasi (2024). By embedding ethics at the system level, BRIM delivers not only technological innovation but also policy relevance, offering actionable RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 128! insights for corporate HR, cybersecurity analysts, and hospital compliance teams. Importantly, this research makes a novel scholarly contribution by proposing an integrated framework that connects behavioral science with AI in real-time cybersecurity contexts, a domain where most existing literature remains fragmented or post-incident oriented. The study supports broader academic discourse by affirming that behavioral data, when interpreted through validated psychological theories such as VSDT and Dark Triad constructs, can enhance digital security frameworks without compromising human dignity. Future Research Recommendations Given the study’s reliance on secondary data, future research should aim to empirically validate the BRIM framework through longitudinal, experimental, or mixed-methods approaches. Controlled deployments within corporate or healthcare environments could assess BRIM’s real-world performance in detecting behavioral anomalies and preventing insider threats. Researchers might explore the impact of cultural context, peer dynamics, or organizational climate as potential moderating variables, factors that could not be fully controlled in the present study. Also, qualitative case studies, including interviews with threat analysts or frontline clinicians, would provide rich narratives to humanize statistical trends and identify latent drivers of behavioral drift. In business contexts, future studies should explore BRIM’s integration into insider threat governance, including its utility during periods of organizational change, such as restructuring or digital transformation. Researchers should assess how BRIM dashboards influence managerial behavior, employee trust, and pre-incident intervention strategies. In healthcare, longitudinal research could evaluate BRIM’s effectiveness in predicting staff burnout and EHR misuse, particularly under stressintensive conditions such as pandemics or disruptions to clinical workflow. Pilot testing in academic medical centers, integrated delivery networks, or telemedicine platforms would provide important generalizability. For defense and national security applications, future work should evaluate BRIM’s ability to detect ideological shifts, revenge motivation, or emotional dissociation in mission-critical personnel, use cases that remain under-researched. Furthermore, studies should explore user acceptance and ethical perceptions of AI-driven behavioral risk profiling among employees, and pose research questions such as How does employee awareness of behavioral AI tools influence organizational trust? or What safeguards are most effective in ensuring perceived fairness and reducing stigma associated with monitoring? Finally, as digital environments evolve, future studies should track how emerging platforms like Metaverse workspaces, AI co-pilots, or emotion-aware wearables might generate new behavioral signals or risk typologies that extend BRIM’s applicability. Final Thoughts This study has demonstrated that behavioral risk indicators such as digital validationseeking, identity confusion, and Dark Triad traits are significant predictors of insider threats (Ohu and Jones, 2025d), especially when contextualized within AI-powered analytic systems like BRIM. This paper also contributes to the literature on criminal psychology by proposing a behaviorally grounded risk assessment framework, and it’s findings underscore that traditional cybersecurity tools, which rely heavily on technical anomalies, may overlook the psychological precursors to deviance that unfold long before a breach or policy violation occurs (Ruohonen & Saddiqa, 2024). By integrating machine learning with forensic cyberpsychology and behavioral ethics, the BRIM framework fills this critical gap, offering a multi-layered, ethically aligned model for predictive insider risk intelligence (Koli et al., 2025). The Behavioral Risk Intelligence Model (BRIM) represents more than a technical innovation and offers a paradigm shift in how insider threats are understood and managed across sectors In business enterprises, these findings reframe insider risk not as isolated OHU & JONES:: Predictive Behavioral Risk Intelligence: An AI Framework for Insider Threat Detection Based on Cognitive and Psychological Indicators ! 129! misconduct but as the outcome of identifiable psychological drift, distress, or disengagement. BRIM helps shift organizations from reactive to proactive, empowering leaders to use behavioral analytics not only to detect potential harm (Arroyabe et al., 2024), but to support employees during sensitive transitions such as restructuring, layoffs, or increased workload. In this way, BRIM reimagines employees not as risks to be surveilled, but as participants in a digital trust ecosystem, thus enabling smarter, safer, and more humane work environments. This transforms traditional insider threat detection into a strategy for ethical risk governance and employee-centered resilience. In healthcare, the implications are particularly urgent. The study suggests that emotional strain, ethical fatigue, or ideological conflict can manifest as risky behavior, often subtly and progressively. Healthcare workers frequently operate under conditions of high stress, emotional fatigue, and systemic constraints (Nagle et al., 2024). BRIM offers a structured and non-punitive method to flag these signals early, especially when integrated with wellness dashboards, audit logs, and clinical governance. This allows healthcare institutions to move beyond compliancedriven responses and toward a culture of care, protecting not only patient data but also the professionals entrusted with it, fostering safer, more compassionate, and accountable care environments. Ultimately, this research affirms that insider threat detection must evolve beyond binary threat models (Al-Mhiqani et al., 2024; NepalBasanta & Basanta Joshi, 2022; Zhang et al., 2021). With tools like BRIM, organizations across sectors can pursue behaviorally intelligent, ethically sound, and context-aware interventions, thereby balancing digital safety and human dignity. BRIM challenges the prevailing narrative of threat detection as reactive and punitive. It offers a forward-looking framework that blends psychological science with AI ethics, supporting proactive intervention, trust-building, and systemic integrity across enterprise and healthcare domains. As organizations increasingly seek holistic, ethical, and intelligent solutions to emerging cyber-behavioral threats, BRIM stands as a timely and transformative contribution to the field. References Abell, L., & Brewer, G. (2014). Machiavellianism, self-monitoring, self-promotion and relational aggression on Facebook. 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