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Cyberattacks from within an organization's trusted entities are known as insider threats. Anomaly detection using deep learning requires comprehensive data, but insider threat data is not readily available due to confidentiality concerns of organizations. Therefore, there arises demand to generate synthetic data to explore enhanced approaches for threat analysis. We propose a linear manifold learning-based generative adversarial network, SPCAGAN, that takes input from heterogeneous data sources and adds a novel loss function to train the generator to produce high-quality data that closely resembles the original data distribution. Furthermore, we introduce a deep learning-based hybrid model for insider threat analysis. We provide extensive experiments for data synthesis, anomaly detection, adversarial robustness, and synthetic data quality analysis using benchmark datasets. In this context, empirical comparisons show that GAN-based oversampling is competitive with numerous typical oversampling regimes. For synthetic data generation, our SPCAGAN model overcame the problem of mode collapse and converged faster than previous GAN models. Results demonstrate that our proposed approach has a lower error, is more accurate, and generates substantially superior synthetic insider threat data than previous models.