Cribl's SecIT Bench: 14 models on 30 SOC/SRE incidents, 20x cost spread
JeremyCMorgan · x · 2026-08-20
Cribl introduced SecIT Bench, a benchmark for evaluating AI agents on real-world IT and security workflows: 14 frontier models tackled 30 realistic incident scenarios, measuring not just correctness but how they investigated, handled uncertainty, and what it cost.
Key findings:
- Diagnostic accuracy showed a relatively narrow spread across models, while investigation cost varied roughly 20x — heavy inference and token spend doesn't necessarily buy trustworthy conclusions.
- Harness design significantly affected results.
- Coding benchmarks can't substitute: debugging a repo has a verifiable answer, while production incidents require reasoning over noisy, incomplete telemetry.
Motivation: teams are spending heavily on AI inference yet still can't fully trust agent conclusions; SecIT Bench aims to give a more rigorous way to compare agents on telemetry workflows.
Related event: Cribl Launches SecIT Bench, Revealing 20x Cost Gap Across AI Models(2 posts)→
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