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arXiv · 2506.04838

On Automating Security Policies with Contemporary LLMs

Pablo Fernández Saura·K. R. Jayaram·Vatche Isahagian·Jorge Bernal Bernabé·Antonio Skarmeta
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Abstract

The complexity of modern computing environments and the growing sophistication of cyber threats necessitate a more robust, adaptive, and automated approach to security enforcement. In this paper, we present a framework leveraging large language models (LLMs) for automating attack mitigation policy compliance through an innovative combination of in-context learning and retrieval-augmented generation (RAG). We begin by describing how our system collects and manages both tool and API specifications, storing them in a vector database to enable efficient retrieval of relevant information. We then detail the architectural pipeline that first decomposes high-level mitigation policies into discrete tasks and subsequently translates each task into a set of actionable API calls. Our empirical evaluation, conducted using publicly available CTI policies in STIXv2 format and Windows API documentation, demonstrates significant improvements in precision, recall, and F1-score when employing RAG compared to a non-RAG baseline.

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Pablo Fernández Saura, K. R. Jayaram, Vatche Isahagian, Jorge Bernal Bernabé, Antonio Skarmeta. 2025-06-05. On Automating Security Policies with Contemporary LLMs. https://arxiv.org/abs/2506.04838

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