David Kenley

Adam Dorian Wong, David Kenley

The Belt & Road Initiative (BRI) is a concerted effort from Ministries under the People's Republic of China (PRC) to diplomatically and economically impose its will upon other nations. El Salvador is a US partner and a beneficiary of foreign investment under the BRI. Recent changes to Salvadoran law do not address the implied risks to the nation's supply-chain and cyber infrastructure. This work addresses the gap by exploring previously limited analysis on BRI activities, its intersection with Salvadoran law, and the national security risks introduced by supply-chain reliance from the BRI. This exploratory study examined a portion of the William & Mary AidData dataset filtered on El Salvador, social media posts, news articles, white papers, and law published by the Legislative Assembly of El Salvador. The analysis suggests that the BRI poses a national security and supply-chain risk to El Salvador through influential-subterfuge, loss of digital sovereignty, which contradicts the State Cybersecurity Agency (ACE) and existing Salvadoran laws. This study provides a foundational understanding and regional context for future research.

Haywood Gelman, John D. Hastings, David Kenley et al.

Insider threats (InTs) within organizations are small in number but have a disproportionate ability to damage systems, information, and infrastructure. Existing InT research studies the problem from psychological, technical, and educational perspectives. Proposed theories include research on psychological indicators, machine learning, user behavioral log analysis, and educational methods to teach employees recognition and mitigation techniques. Because InTs are a human problem, training methods that address InT detection from a behavioral perspective are critical. While numerous technological and psychological theories exist on detection, prevention, and mitigation, few training methods prioritize psychological indicators. This literature review studied peer-reviewed, InT research organized by subtopic and extracted critical theories from psychological, technical, and educational disciplines. In doing so, this is the first study to comprehensively organize research across all three approaches in a manner which properly informs the development of an InT education platform.

Haywood Gelman, John D. Hastings, David Kenley

Insider threats are a growing organizational problem due to the complexity of identifying their technical and behavioral elements. A large research body is dedicated to the study of insider threats from technological, psychological, and educational perspectives. However, research in this domain has been generally dependent on datasets that are static and limited access which restricts the development of adaptive detection models. This study introduces a novel, ethically grounded approach that uses the large language model (LLM) Claude Sonnet 3.7 to dynamically synthesize syslog messages, some of which contain indicators of insider threat scenarios. The messages reflect real-world data distributions by being highly imbalanced (1% insider threats). The syslogs were analyzed for insider threats by both Sonnet 3.7 and GPT-4o, with their performance evaluated through statistical metrics including accuracy, precision, recall, F1, specificity, FAR, MCC, and ROC AUC. Sonnet 3.7 consistently outperformed GPT-4o across nearly all metrics, particularly in reducing false alarms and improving detection accuracy. The results show strong promise for the use of LLMs in synthetic dataset generation and insider threat detection.