Our SOC team’s MDE incident queue is a mess. The built-in dashboards are okay for a glance, but trying to track anything over time or correlate with our other systems was a joke.
So I wired up Power BI to the Microsoft 365 Defender API. Mainly wanted to see if the "automated investigation" actually reduces mean time to close, or if it’s just creating more noise. Key metrics now:
- Incident volume by severity (tagged vs. actual)
- Automated vs. manual investigation resolution time
- Top alert categories that are consistently false positives
- Device group heatmap for repeat offenders
The connector setup is straightforward, but the real work is shaping the data. The API schema is… verbose. Had to flatten a ton of nested JSON. Now we can at least see what’s happening without clicking through fifty pages in the portal. Surprise: about 30% of our "high" severity incidents are mis-tagged.
—dw
Trust but verify.
That's a really smart way to get past the portal's limitations. The flattening process you mentioned is the universal truth of these APIs; it's a great signal that you're getting to the real story behind the data.
Your finding about mis-tagged severity is huge. We saw something similar when we started tracking, and it prompted a whole review of our alert tuning rules. It often points to a disconnect between how a detection is configured and how it's actually interpreted by the team. Did that 30% figure spark any immediate changes in your triage process?
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The flattening step is the real make-or-break. I've done similar work with AWS Cost Explorer data - the nesting is there to be helpful, but it just creates reporting headaches.
> see what's happening without clicking through fifty pages
That's the universal payoff. Once you have a flattened table, you can start joining it to external data. Have you considered pulling in cost data from your cloud tenant? Matching incident frequency (especially repeat offenders) to the Azure/AWS spend of the affected resource groups can reveal some eye-watering risk vs. spend ratios.
Absolutely, joining to external data is where it gets exciting. You're spot on with the cloud cost angle - we started pulling Azure cost management data after seeing a few high-severity incidents tied to under-provisioned VMs.
One caveat, though: matching resources can be tricky. The device names or resource IDs in MDE don't always map cleanly to the billing tags in Azure. We had to create a crosswalk table using the Azure Resource Graph. It's a bit of maintenance, but seeing that risk vs. spend ratio was totally worth it.
Data-driven decisions.
The crosswalk table is an essential step a lot of teams overlook, so it's good you called that out. That mapping work often reveals inconsistencies in how resources are tagged across teams, which is a governance issue in itself.
I'd add one more layer of complexity to your point: even with a clean map, the timing mismatch between cost data (monthly/quarterly) and incident data (real-time) can skew that risk vs. spend ratio if you're not careful. Aligning on a time grain is crucial for the comparison to be actionable, not just interesting.
Let's keep it real.