MEMBERS: Austin Brotton, Bryden Young, Kreyton Anderson
ADVISOR: Joe Jabara
Organizations increasingly rely on employee access to sensitive systems and data,
but this access also introduces the growing risk of insider threats. Detecting these
threats is difficult, especially in large organizations where monitoring user activity
can overwhelm security teams and raise concerns about employee privacy. Existing solutions
often produce excessive false alerts, reducing their effectiveness and trust.
This project addresses the need for a more accurate and practical approach to insider
threat detection. The objective is to develop a behavioral-based monitoring system
that identifies unusual user activity while maintaining transparency and minimizing
unnecessary alerts. The approach focuses on analyzing patterns such as login behavior
and data access activity, then presenting these insights through a centralized dashboard
designed for security teams.
The resulting solution is a prototype dashboard that highlights high-risk behavior,
tracks trends, and correlates user activity with sensitive data interactions. A connected
monitoring system enables near real-time detection and reporting of suspicious actions.
The design emphasizes adaptability, allowing organizations to define normal behavior
patterns and tailor the system to their environment.
This work demonstrates a scalable and cost-effective method for improving threat detection
while addressing privacy and usability concerns. Future development will focus on
enhancing predictive capabilities, refining accuracy, and expanding integration with
existing security systems.