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arXiv · 2511.14908

On-Premise SLMs vs. Commercial LLMs: Prompt Engineering and Incident Classification in SOCs and CSIRTs

Gefté Almeida·Marcio Pohlmann·Alex Severo·Diego Kreutz·Tiago Heinrich·Lourenço Pereira
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Abstract

In this study, we evaluate open-source models for security incident classification, comparing them with proprietary models. We utilize a dataset of anonymized real incidents, categorized according to the NIST SP 800-61r3 taxonomy and processed using five prompt-engineering techniques (PHP, SHP, HTP, PRP, and ZSL). The results indicate that, although proprietary models still exhibit higher accuracy, locally deployed open-source models provide advantages in privacy, cost-effectiveness, and data sovereignty.

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Gefté Almeida, Marcio Pohlmann, Alex Severo, Diego Kreutz, Tiago Heinrich, Lourenço Pereira. 2025-11-18. On-Premise SLMs vs. Commercial LLMs: Prompt Engineering and Incident Classification in SOCs and CSIRTs. https://arxiv.org/abs/2511.14908

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