This paper addresses the challenge of detecting insider threats in cybersecurity by proposing behavior model-driven approaches. It argues that existing datasets are incapable to capture nuanced user activities accurately and proposes an enhanced dataset generated by more elegant structure. The paper discusses the evolving threat situations and the need for proactive cybersecurity measures, presents a taxonomy of insiders, and emphasizes the importance of behavior-driven approaches. It mentions existing datasets limitations and introduces the proposed data generator structure, explaining its components and implementation logic. The paper illustrates a use case showcasing the application of generated data for insider threat identification. It concludes by stressing the significance of behavior-driven approaches and high-quality datasets in enhancing detection capabilities against insider threats.