Mitigating Insider Threats in SaaS PEO Applications through Behaviour-Based Access Control

Abstract

The increasing reliance on SaaS-based Professional Employer Organization (PEO) applications for human resource management has exposed organizations to a critical category of cybersecurity risk: insider threats. These threats, originating from trusted users with legitimate access, are often difficult to detect and mitigate using traditional Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) models. In this study, we propose a Behavior-Based Access Control (BBAC) framework designed to dynamically adapt user access rights based on behavioral analytics and anomaly detection. Our model continuously monitors user behavior across parameters such as access frequency, time of access, geolocation, and data sensitivity to identify potential deviations that may indicate malicious intent. Using a simulated SaaS PEO environment, we implemented and evaluated the proposed BBAC system using machine learning algorithms such as Isolation Forest and Long Short-Term Memory (LSTM) networks for anomaly detection. The experimental results demonstrate that the BBAC model significantly outperforms traditional access control approaches in identifying and mitigating insider threats with high precision and low false-positive rates. This research underscores the importance of integrating adaptive, behavior-driven controls into cloud-native HR systems to enhance organizational security and trust.