A detailed case study from a Chinese SOC team reveals how they slashed false positive rates from 33% to 7% using a multi-model AI approach. The team found that relying solely on Claude for alert triage led to inconsistent results, particularly for nuanced security events. Instead, they deployed a pipeline: a lightweight model for initial alert classification, a specialized model for context enrichment (pulling IP reputation, user behavior, Slack threads), and Claude only for final decision support. This layered architecture reduced analyst fatigue and improved mean time to respond. The post includes specific metrics and trade-offs, such as latency vs. accuracy for different model choices. For engineering leaders, this offers a concrete blueprint for AI-assisted SOC operations without over-investing in a single LLM.
A Chinese engineering team describes how they reduced SOC false positive rates from 33% to 7% by combining multiple AI models for alert enrichment and triage. The key insight is that Claude alone wasn't enough; a layered approach with specialized models for different alert types was critical. This is a rare detailed case study from a real SOC environment.