(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations
arXiv:2607.26201
The paper presents (EC)2, a multi‑agent framework that uses large language models to generate event‑centric, hypothesis‑driven explanations for cybersecurity alerts, improving analysts' ability to investigate and classify incidents.
Abstract
Security operations centers rely on anomaly detection systems to flag suspicious events. Feature-level explanations for anomaly detectors offer limited value for operational investigations. To effectively handle alerts, analysts need to know contextual relationships and need actionable understanding of the entities involved. This paper introduces an event-centric detector-agnostic approach for explaining cybersecurity alerts in small- to medium-sized enterprise networks. We present (EC)2, a multi-agent framework that performs structured, hypothesis-driven investigation to provide explanations grounded in verifiable evidence. Evaluation results show that the proposed framework improves post-detection analysis by generating operationally meaningful explanations, which also enhance event classification accuracy.
21 pages