Less Data, More Security: Advancing Cybersecurity LLMs Specialization via Resource-Efficient Domain-Adaptive Continuous Pre-training with Minimal Tokens
arXiv:2507.02964v2 Announce Type: replace Abstract: The increasing scale of AI workloads demands High-Performance Computing (HPC) infrastructure and training methodologies that are both scalable and sustainable. While Large Language Models (LLMs) demonstrate exceptional natural language capabilities, general-purpose models often lack the specialized domain knowledge necessary for effective cybersecurity analysis. We investigate Domain-Adaptive Continuous Pretraining (DAP) as a scalable, resource-efficient methodology for enhancing cybersecurity understanding in pretrained LLMs, implemented thr
The rapid deployment of general-purpose LLMs highlights their limitations in specialized, high-stakes domains like cybersecurity, necessitating targeted solutions for practical application and resource efficiency.
Developing resource-efficient, specialized LLMs is critical for effective and accessible AI-driven cybersecurity, reducing the compute burden while improving accuracy for domain-specific tasks.
This paper demonstrates a methodology for significantly improving the specialization and security capabilities of LLMs with minimal data and computational resources, making advanced AI cybersecurity more attainable.
- · Cybersecurity firms
- · Organizations with limited compute resources
- · AI model developers
- · Cloud providers
- · General-purpose LLM providers for niche applications
- · Companies reliant on outdated cybersecurity methods
More robust and accessible AI-driven cybersecurity solutions become deployable across a wider range of organizations.
Reduced attack surface and improved threat detection capabilities lead to a measurable decrease in successful cyberattacks.
The methodology could be extended to other high-stakes domains, accelerating the development of specialized, efficient AI across various industries.
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Read at arXiv cs.CL