Taming Data Challenges in ML-based Security Tasks Using Generative AI

Researchers propose using Generative AI to augment training datasets with synthetic data, improving machine learning security classifiers by up to 32.6% even with minimal training samples. The study evaluates six state-of-the-art GenAI methods across seven security tasks and introduces Nimai, a novel controlled data synthesis scheme, while identifying limitations in GenAI applicability to certain security domains.

Machine learning classifiers power critical security infrastructure, yet their performance remains constrained by data availability and quality issues rather than algorithmic limitations alone. This research redirects focus toward data challenges, demonstrating that synthetic data generation via GenAI can meaningfully enhance classifier robustness in realistic, resource-constrained environments.

The practical significance emerges from the 32.6% performance improvement achieved with only ~180 training samples—a constraint common in specialized security domains where labeled data is expensive or sensitive. The introduction of Nimai represents progress toward controllable synthesis, addressing concerns about GenAI-produced data quality and validity. Additionally, the ability to rapidly adapt to concept drift with minimal labeling suggests deployment advantages in evolving threat landscapes.

However, the research candidly reports that some GenAI schemes fail initialization on certain tasks, and specific characteristics—noisy labels, overlapping class distributions, sparse features—actively hinder improvement. This nuance matters significantly: GenAI is not a universal solution but a tool with task-dependent effectiveness. Security practitioners should avoid assuming synthetic data uniformly strengthens classifiers.

Looking forward, the study establishes a research agenda for GenAI tools explicitly engineered for security domains rather than adapted from general-purpose applications. Organizations deploying ML-based security systems should monitor emerging GenAI methodologies designed specifically for their threat models while validating synthetic training data against real-world performance metrics. The convergence of GenAI and security ML represents an important efficiency frontier, particularly for enterprises lacking massive labeled datasets.

• →GenAI-generated synthetic data improves security classifier performance by up to 32.6% in severely data-constrained environments with only ~180 training samples.
• →Nimai, a novel controlled data synthesis scheme, enables more reliable synthetic data generation compared to standard GenAI methods.
• →GenAI facilitates rapid adaptation to concept drift post-deployment with minimal additional labeling requirements.
• →Noisy labels, overlapping class distributions, and sparse feature vectors limit GenAI effectiveness on certain security tasks.
• →Some GenAI schemes fail to initialize on specific security tasks, indicating task-dependent effectiveness rather than universal applicability.