LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge
This work addresses the efficiency problem for Security Operations Center analysts by automating threat intelligence generation, though it is incremental as it applies existing LLM capabilities to a specific domain.
The paper tackles the manual, labor-intensive process of generating organization-specific threat intelligence by introducing LocalIntel, an automated framework that leverages Large Language Models to contextualize global threat reports with local knowledge, achieving up to 93% accuracy in threat intelligence generation.
Security Operations Center (SoC) analysts gather threat reports from openly accessible global threat repositories and tailor the information to their organization's needs, such as developing threat intelligence and security policies. They also depend on organizational internal repositories, which act as private local knowledge database. These local knowledge databases store credible cyber intelligence, critical operational and infrastructure details. SoCs undertake a manual labor-intensive task of utilizing these global threat repositories and local knowledge databases to create both organization-specific threat intelligence and mitigation policies. Recently, Large Language Models (LLMs) have shown the capability to process diverse knowledge sources efficiently. We leverage this ability to automate this organization-specific threat intelligence generation. We present LocalIntel, a novel automated threat intelligence contextualization framework that retrieves zero-day vulnerability reports from the global threat repositories and uses its local knowledge database to determine implications and mitigation strategies to alert and assist the SoC analyst. LocalIntel comprises two key phases: knowledge retrieval and contextualization. Quantitative and qualitative assessment has shown effectiveness in generating up to 93% accurate organizational threat intelligence with 64% inter-rater agreement.