SIR-Bench: Evaluating Investigation Depth in Security Incident Response Agents (Apr 2026)

Title: SIR-Bench: Evaluating Investigation Depth in Security Incident Response Agents (Apr 2026)
Link: http://arxiv.org/abs/2604.12040v1
Date: April 2026

Summary:
This paper introduces SIR-Bench, a benchmark comprising 794 test cases for evaluating autonomous security incident response agents. It focuses on distinguishing genuine forensic investigation from 'alert parroting' by measuring an agent's ability to discover novel evidence. The authors also present 'Once Upon A Threat' (OUAT), a framework that replays real incident patterns in controlled cloud environments to produce authentic telemetry for evaluation.

Key Topics:
- Security Incident Response
- Autonomous Agents
- Cloud Security
- LLM Benchmarking
- Forensic Investigation
- Telemetry Analysis
- Cybersecurity AI

Chapters:
00:00 - Introducing SIR-Bench Benchmark
01:23 - Defining Alert Parroting
02:45 - Critiquing Existing Benchmarks
04:17 - Designing the OUAT Framework
05:47 - Scaling Realistic Attack Data
06:51 - Simulating False Positives
07:50 - Measuring Triage Accuracy
10:00 - Superhuman Detection Performance
11:11 - Evaluating Investigation Depth
12:30 - Eliminating LLM Judge Bias
14:40 - Quantifying Tool Usage
15:43 - Addressing Telemetry Blindspots
17:31 - Scaling to Multi-Cloud
18:48 - Predicting Adversarial Adaptation

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