tailwindlabs / tailwindcss
github.com·21h ago
Developer Tools
Databricks Blog·29d ago·5 min read
Scaling Security Alert Triage With Specialized Agents on Databricks
TL;DR
Databricks developed specialized agents to automate the triage of low-severity security alerts, addressing the challenge of identifying real threats among thousands of daily alerts. Initial attempts using a foundation model resulted in a 50% escalation rate, which was deemed ineffective.
✦ Why It Matters
Engineers can implement specialized agents to automate low-severity alert triage, improving overall security response efficiency.
Key Takeaways
- Explore using specialized agents for alert triage.
- Refine alert prioritization methods to reduce noise.
- Test different models for better accuracy in triage.
- Monitor the impact of automation on analyst workload.
Full Summary
Databricks processes vast amounts of security logs from various sources, creating a centralized alerts table for Incident Response (IR) analysts. Traditionally, the IR team prioritized high and medium severity alerts, often neglecting low severity due to their volume and low fidelity.
To enhance efficiency, Databricks implemented specialized agents capable of reasoning and making judgment calls on alerts. Initially, they tested a foundation model by inputting all relevant alert data into a single prompt, which led to a 50% escalation rate, indicating that half of the alerts were still noise.
This highlighted the need for a more refined approach to triage. The aim is to develop a system that accurately identifies which low-severity alerts require further investigation, ultimately improving the alert triage process and reducing the burden on human analysts.