Building an AI-powered incident response workflow in n8n
Build an AI-powered incident response workflow with n8n. Combine RAG, threat intelligence, and historical incidents to accelerate SOC investigations. Viraj led Customer Success at n8n where he helped enterprise customers in telecoms, government, finance and other industries adopt n8n and roll it out successfully across key departments.
Key Takeaways
- He spent a lot of time working with Cybersecurity leaders and draws on this experience below.
Dmitry is a Security Automation and Detection Engineer with a background in detection engineering, SecOps automation and cloud security.
- burned-out analysts head for the exit - high employee turnover This post introduces an automation framework built to address these challenges, while offering risk-conscious Cybersecurity Engineering Managers a way to leverage AI in their automation workflows.
Something which from our experience, many managers have been rightly cautious about doing.
- And with analyst turnover at 10-25% in half of SOCs (1), that knowledge walks out the door when people do.
The framework uses Retrieval Augmented Generation (RAG) pipelines to capture that reasoning automatically and put it back in front of analysts at the moment they need it, with no extra effort on their part.
- The interest is obvious - surveys keep reporting that 50-90% of organizations are "exploring" or "planning" AI use in the SOC (2).
The repository below is built around that: automate the repetitive, non-creative work, keep useful context close to the analyst, and let teams dial AI up or down based on their risk tolerance and where they want humans in the loop.
- The payload is ingested and triggers three parallel retrievals: the closest matching reference playbook, similar resolved incidents from your historical record, and current threat intelligence from the web.
Stats & Key Facts
- #And with analyst turnover at 10-25% in half of SOCs (1), that knowledge walks out the door when people do.
- #The interest is obvious - surveys keep reporting that 50-90% of organizations are "exploring" or "planning" AI use in the SOC (2).
He spent a lot of time working with Cybersecurity leaders and draws on this experience below. Dmitry is a Security Automation and Detection Engineer with a background in detection engineering, SecOps automation and cloud security. He has spent several years on the blue team side of the stack building and reviewing security tooling across cloud environments and draws on this experience as co-author.
Incident response teams are dealing with a set of problems that rarely announce themselves. They build up in the background until they show up where it hurts: a climbing mean time to resolution (MTTR) an increased dollar cost from attacks that should have been shut down seconds after detection. burned-out analysts head for the exit - high employee turnover This post introduces an automation framework built to address these challenges, while offering risk-conscious Cybersecurity Engineering Managers a way to leverage AI in their automation workflows.
Something which from our experience, many managers have been rightly cautious about doing. We built it around three aspirations we kept running into across the SOCs we worked with: 1. Reduce the time to capture and reuse what worked last time.
For more details please read the original article at n8n Blog.
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