Business Risk Analytics for Insider Threat Prevention in Data-Driven Organizations: An Entropy-Based Decision Framework
Main article
Abstract
Insider threat prevention has become a business analytics problem as much as a technical cybersecurity problem. Data-driven organizations depend on privileged employees, cloud platforms, analytics pipelines, and shared data assets, yet conventional insider threat programs often emphasize post-incident detection rather than pre-incident risk measurement. This study develops an entropy-based decision framework for business risk analytics that converts multi-source organizational indicators into interpretable insider risk scores and prevention priorities. The proposed framework integrates human behavior indicators, organizational management conditions, technical safeguard maturity, and data asset exposure into a unified business risk index. Instead of treating insider threat as a binary security event, the framework evaluates the uncertainty embedded in risk indicators and uses entropy weighting to identify which factors contribute most strongly to residual business exposure. A simulated organizational dataset of 640 employee-role observations across five business units is used to demonstrate the framework, including indicator normalization, entropy weight estimation, risk segmentation, mitigation scenario analysis, and managerial decision mapping. Results show that data asset criticality, access-control weakness, abnormal work-pattern signals, and role-pressure indicators generate the largest weighted contributions to insider risk. Scenario analysis further indicates that targeted mitigation focused on the top 25% of high-contribution indicators reduces the overall business risk index by 31.8%, while broad but unfocused controls reduce it by only 12.4%. The study contributes a practical analytics framework that supports early warning, explainability, and resource allocation for insider threat prevention in data-driven organizations.
