Using Genai for Synthetic Data Generation in Cybersecurity and Compliance

Abstract

The increasing nature of the cybersecurity threats and high-pressure requirement of the norms compliance have resulted in the need to think differently about how data is managed and secured. Generative Artificial Intelligence (GenAI) provides a potential solution, incorporating the formulation of artificial data and developing these data with a high level of safety, privacy, and confidentiality, which implies guaranteed testing, training, and compliance auditing without any security concerns. GenAI models produce synthetic data that mimics the statistics of unprecedented data, allowing the security field to use generated data without revealing sensitive data to vulnerable access; this makes it particularly appealing in techniques as threat modelling, anomaly detection, simulators of red-teaming, and similar ones. Moreover, artificial datasets can be utilized in ensuring regulatory adherence because they enable testing of systems across various environments based on the type of rules that should govern data use. In spite of this potential, synthetic data generation in GenAI has various challenges, such as data fidelity, bias reprocessing, and regulatory objects. Nevertheless, the progress of GenAI models like GAN, transformers, and diffusion models takes place and enhances the reliability and security of generated data. This paper addresses how GenAI helps in cybersecurity and compliance, evaluates the advantages and disadvantages of the technology, and reflects on recent research and practical opportunities flag identify possible ways to integrate the new technology through the lens of current studies and real-life examples.