arXiv:2507.06092v4 Announce Type: replace-cross Abstract: Machine learning-based supervised classifiers are widely used for security tasks, and their improvement has been largely focused on algorithmic advancements. We argue that data challenges that negatively impact the performance of these classifiers have received limited attention. We address the following research question: Can developments in Generative AI (GenAI) address these data challenges and improve classifier performance? We propose augmenting training datasets with synthetic data generated using GenAI techniques to improve class
The rapid advancement and accessibility of Generative AI capabilities are enabling their application to long-standing data challenges across various domains, including cybersecurity.
This development proposes a direct method to enhance the reliability and effectiveness of AI-driven security systems by addressing critical data limitations, moving beyond algorithmic improvements.
The focus for improving ML-based security shifts from solely algorithmic advancements to also include strategic data augmentation using Generative AI, potentially standardizing synthetic data generation as a security best practice.
- · Generative AI developers
- · Cybersecurity solution providers
- · Organizations with limited security data
- · Adversaries relying on data-centric attack vectors
- · Traditional data collection methodologies
ML-based security systems will exhibit higher performance and robustness due to improved training data.
The proliferation of synthetic data generation tools could lead to new challenges in distinguishing real from artificially generated data in defensive and offensive contexts.
Enhanced security capabilities powered by GenAI might enable more complex and adaptable autonomous defense systems, further escalating the AI arms race in cybersecurity.
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Read at arXiv cs.LG