Arctic Wolf Describes Its Evaluation Process for the Aurora Agentic SOC
Summary
Arctic Wolf describes five AI Trust Engine checkpoints for its Aurora Agentic SOC, including validated test datasets, offline and model-graded evaluations, staged production rollout, and human feedback. Its performance and ROI figures are company claims.
Key points
- Arctic Wolf says it benchmarks model, prompt, and workflow changes against curated historical investigations with validated outcomes before production.
- Its offline evaluations consider verdict accuracy alongside response speed and compute cost; changes must meet internal quality thresholds.
- A grading framework evaluates production responses for factors including hallucinations, tool correctness, reasoning consistency, and the usefulness of incident summaries and recommendations.
- The company describes a rollout from silent mode, to analyst-reviewed recommendations, to approved autonomous actions, with low-confidence cases escalated to humans.
- Analyst feedback and sampled reviews of autonomous investigations are added to the benchmark datasets for future evaluations.
- Arctic Wolf claims the Swarm of Experts resolves more than 60% of cases weekly without human intervention.
Article Details
- Topic
- Arctic Wolf’s AI Trust Engine for evaluating and governing the Aurora Agentic SOC
Vendors
Products
AI Trust EngineWe call it the AI Trust Engine™, and here’s how we built it.Aurora Agentic SOCThat history runs deep, as the Aurora Agentic SOC has resolved more than 3 million cases since launch in March 2026, and the pre-agentic version of our SOC saw analysts resolve tens of millions more over the 14 years ofAurora Superintelligence PlatformSpeed mattered, and so did value, but more than anything, we built the Aurora® Agentic SOC on the Aurora Superintelligence Platform to be something our customers could trust.Swarm of ExpertsBefore an agent acts inside a customer environment, it follows a controlled path into the Swarm of Experts™, our patent-pending agentic framework.