Generating semi-automated security playbooks for vulnerability mitigation from unstructured advisory data

Abstract

Automated security risk management addresses threats by implementing mitigation and remediation strategies described in public security advisories. These advisories, regularly published by independent Cyber Emergency Response Teams (CERTs), are typically presented as unstructured text and supplemented with additional security-relevant information sources. Using a Large Language Model (LLM) orchestration framework, we generate security playbooks in a standardized meta-model, visualize them with Business Process Modeling and Notation (BPMN) diagrams, and classify them into actionable tasks. We present the theoretical foundations of the framework, describe the underlying meta-model, and conduct an experimental study that produces a dataset of 725 security playbooks. Our subsequent content analysis shows that advisory-based playbooks focus on update tasks, but also include defensive themes like disabling vulnerable policies or restricting network access.