Risk-Weighted Access Control Integrating Least-Privilege with Context-Aware Authentication Signals

Modern enterprise information systems increasingly operate in distributed and cloud-based environments where traditional access control mechanisms struggle to address dynamic security risks. Conventional authorization models such as Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) primarily rely on static policy definitions and therefore lack the ability to adapt to contextual authentication conditions during access requests. This study proposes a Risk-Weighted Access Control (RWAC) framework that integrates least-privilege policy enforcement with context-aware authentication analytics to enable adaptive authorization decisions. The proposed model computes a contextual risk score by aggregating heterogeneous authentication signals, including device trust level, geographic location variance, network reputation indicators, and behavioral anomaly metrics. A mathematical risk evaluation function is introduced to dynamically assess authentication contexts and determine access authorization based on predefined risk thresholds. Experimental evaluation compares the RWAC model with RBAC, ABAC, and Risk-Adaptive Access Control (RAdAC) baselines using standard performance metrics such as precision, recall, F1-score, and detection latency. The results demonstrate that the RWAC framework improves the detection accuracy of high-risk authentication attempts while reducing false positive access blocks and maintaining lower decision latency. Furthermore, the model aligns with Zero Trust security principles by continuously verifying authentication context before granting privileges. The findings highlight the effectiveness of integrating contextual risk analytics with least-privilege enforcement to enhance enterprise access control security.