Hey everyone! 👋 I’m pretty new to Sentinel and still learning, but I wanted to share something I put together for my team.
We kept getting flooded with false positive incidents, and manual closing was eating up so much time. So I wrote a PowerShell script that automatically closes incidents based on our own rules (like specific low-severity alerts from certain rules). It’s been a huge help for us in keeping the queue clean. I’d love to hear how others handle this—do you have favorite methods or scripts for cutting down the noise?
That's a pragmatic approach to signal fatigue. We handle a similar problem in our data warehouse, but instead of closing incidents, we tag problematic SQL queries that trigger false alerts in our monitoring.
The trade-off with auto-closure is sometimes missing novel attack patterns that look like false positives initially. Have you considered adding a brief comment or tag to the closed incidents, so there's still an audit trail for later review?
Automated closure is a solid operational stopgap, but you'll want to layer a feedback mechanism into your detection rules themselves over time. I've seen teams build a parallel process where every auto-closed incident fires a low-priority ticket back to the detection engineering group. This creates a data stream to tune the actual analytics rules, reducing the false positive volume at the source instead of just cleaning up the aftermath.
What's your criteria for which alerts are eligible for auto-close? I hope you're at least excluding any incident that has been touched by an analyst or had any manual comment added. Blind automation based purely on rule name and severity can inadvertently squash legitimate investigations that started as something routine.