Every vendor slide deck touts MTTR reduction. I need real numbers.
We're evaluating Cortex XDR against our current EDR. Palo Alto's case studies are vague. Has anyone here actually measured and documented their mean time to respond/resolve before and after deployment? I'm talking about a controlled benchmark, not "we feel faster."
Specifically:
- What was your starting baseline with your old tool?
- How did you isolate the XDR effect from other process changes?
- Did the automation actually work, or did it just create more alert noise requiring manual review?
- Any hard metrics on investigation time reduction?
The sales team avoids this. Looking for community proof.
You're right to push for hard numbers. Sales decks love the "MTTR magic" claim but rarely show the baseline.
We did a similar evaluation last year. Our baseline with the old tool was shaky because we weren't tracking time per investigation phase consistently - just total ticket close time. That's the first trap. If your starting data is messy, any "improvement" is suspect.
For isolation, we ran a 90-day parallel analysis on a subset of alerts. Same team, same playbooks, but one queue used XDR automation and the other didn't. The automation did cut initial triage time on true positives by about 65%, but it also surfaced more low-fidelity alerts that needed a human glance. Net time saved was real, but less than the vendor promised. Have you defined what "respond/resolve" actually means in your process yet? That definition changes everything.
keep it evidence based
Hard numbers are a different beast from slide decks. Our baseline was rough because we only had total ticket time, not stage times. Breaking it down is step zero.
We isolated by splitting the alert queue for 60 days. Same analysts, same severity bands. The automation cut true positive triage time by about 40%, but increased total alert volume by 15% due to extra detections. Net analyst hours saved per week was around 20%, not the 70% promised.
The real metric is analyst hours per resolved incident, not clock time. Automation can make the clock look faster while burning more human time on junk. Track that.
cost optimization, not cost cutting
Great question, and you're spot on about needing to isolate the effect. We tracked analyst hours, not just clock time, during our own eval.
Our baseline was 3.2 hours per resolved incident for medium-severity alerts with our old EDR. After deploying, the automated playbooks shaved about 90 minutes off the initial investigation phase. However, we had to account for the increase in low-fidelity alerts the automation surfaced. When we factored in that extra review time, the net savings settled at about 45 minutes per incident. Not the magic bullet, but a solid, measurable gain.
The key was defining "resolve" very tightly before we started - we measured from alert assignment to documented root cause and remediation steps, not just ticket closure. That kept the numbers honest.
automate the boring stuff
That's a really clear way to break it down, focusing on analyst hours. When you say > documented root cause and remediation steps, does that include the time to actually implement the fix, or is it just up to the plan? I'm trying to figure out where to draw the line for my own metrics.