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This study was undertaken to pilot a Private AI curriculum designed with a problemcentered instruction (PCI) approach for post-secondary Computer Science (CS) education. To this end, a condensed version of one of the ten curricular modules was implemented in a twohour workshop. The mixed-method data analysis revealed participants' positive motivation and interest in the curriculum, while also pinpointing opportunities to further improve the design strategies of the curriculum. 

Insider Threat is a significant and potentially dangerous security issue in corporate settings. It is difficult to mitigate because, unlike external threats, insiders have knowledge of an organization’s access policies, access hierarchy, access protocols, and access scheduling. In addition, the complexity, time, and skill required to locate the threat source, model, and timestamp make it more difficult for organizations to combat. Several approaches to reducing insider threat have been proposed in the literature. However, the integration of access control and moving target defense (MTD) for deceiving insiders has not been adequately discussed. In this paper, we combine MTD, deception, and attribute-based access control to make it more difficult and expensive for an insider to gain unauthorized access. We introduce the concept of correlated attributes into ABAC and extend the ABAC model with MTD by generating mutated policy using the correlated attributes for insider threat mitigation. The evaluation results show that the proposed framework can effectively identify correlated attributes and produce adequate mutated policy without affecting the usability of the access control systems.