Towards Agentic Investigation of Security Alerts

Security analysts are overwhelmed by the volume of alerts and the low context provided by many detection systems. Early-stage investigations typically require manual correlation across multiple log sources, a task that is usually time-consuming. In this paper, we present an experimental, agentic workflow that leverages large language models (LLMs) augmented with predefined queries and constrained tool access (structured SQL over Suricata logs and grep-based text search) to automate the first stages of alert investigation. The proposed workflow integrates queries to provide an overview of the available data, and LLM components that selects which queries to use based on the overview results, extracts raw evidence from the query results, and delivers a final verdict of the alert. Our results demonstrate that the LLM-powered workflow can investigate log sources, plan an investigation, and produce a final verdict that has a significantly higher accuracy than a verdict produced by the same LLM without the proposed workflow. By recognizing the inherent limitations of directly applying LLMs to high-volume and unstructured data, we propose combining existing investigation practices of real-world analysts with a structured approach to leverage LLMs as virtual security analysts, thereby assisting and reducing the manual workload.

LLM-Powered Intent-Based Categorization of Phishing Emails

Phishing attacks remain a significant threat to modern cybersecurity, as they successfully deceive both humans and the defense mechanisms intended to protect them. Traditional detection systems primarily focus on email metadata that users cannot see in their inboxes. Additionally, these systems struggle with phishing emails, which experienced users can often identify empirically by the text alone. This paper investigates the practical potential of Large Language Models (LLMs) to detect these emails by focusing on their intent. In addition to the binary classification of phishing emails, the paper introduces an intent-type taxonomy, which is operationalized by the LLMs to classify emails into distinct categories and, therefore, generate actionable threat information. To facilitate our work, we have curated publicly available datasets into a custom dataset containing a mix of legitimate and phishing emails. Our results demonstrate that existing LLMs are capable of detecting and categorizing phishing emails, underscoring their potential in this domain.