Before You Hand Over the Wheel: Evaluating LLMs for Security Incident Analysis

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• arXiv: 2603.06422
• DOI: https://doi.org/10.48550/arXiv.2603.06422
• Submitted: 2026-03-06
• Source URL: https://arxiv.org/abs/2603.06422

Abstract Summary

The paper introduces SIABENCH, an agentic evaluation framework for Security Incident Analysis (SIA). It builds a dataset with deep incident analysis workflows and alert-triage tasks, then benchmarks major LLMs on network and memory forensics, malware analysis, phishing analysis, log analysis, and false-alert detection.

Why It Matters For The Wiki

This is directly relevant to AI SOC evaluation. It can strengthen research questions around operational outcome metrics, alert-triage realism, autonomous investigation failure modes, and whether SOC agents should be trusted before rigorous replay-style testing.

Recommended Ingest Notes

• Create source note under 01_Sources/.
• Extract evidence around SIABENCH task taxonomy, dataset shape, and autonomous SIA evaluation risks.
• Compare against existing RQ - Telemetry Provenance Contract For Agentic SOC and RQ - Self-Improving SOC Agent Prompt Governance.