Synthetic data generation, a process where datasets are generated via computer algorithms that don’t compromise privacy are shown to near-perfectly paradigm the abstract and complex relationships between data items. We can generate a diverse and massive quantity of training data with little effort by artificially simulating genuine data sets with synthetic data. By utilizing a conditional tabular generative adversarial network, it is possible to create synthetic data that mimics real-world cloud data while maintaining the network’s integrity. Once the real log data has been preprocessed, identification of key features and their distributions will guide the synthetic generation process. CTGAN can handling imbalanced data, capturing complex distributions while improving the overall stability of the data. In CTGAN, the generator improves by trying to maximize the probability of the discriminator making mistakes, while the discriminator enhances its accuracy in identifying fake samples. The use of these methods for generating realistic data showcases potential in the ability to accurately generate cloud logs datasets which are nearly indistinguishable from real testing data collected from the real world.