Great question, and a super practical one too. Tracking that "investigation scope" cost isn't always standardized, but you have to make it part of your evaluation.
For a cloud bill, you can often create a separate cost allocation tag for queries the SOC tool initiates. When you're doing a POC, ask the vendor for their average "data scan volume per investigation" or similar. The scary part is when their "AI enrichment" silently runs a massive historical query across petabytes of logs for every single alert. That's what blows the budget.
Absolutely push them on API call counts and estimated data processing costs. If they can't give you a clear, auditable breakdown of the resources *their* investigation consumes, that's a major red flag. You're trading alert fatigue for unpredictable spend, which isn't much better.
Absolutely. That point about demanding per-use-case matrices is correct, but in practice vendors will fight you tooth and nail because it exposes the weak spots they've papered over with aggregated stats. I've had them claim "proprietary detection methods" as a reason not to share.
You need to bake the requirement for per-alert-type metrics into the contract before the POC starts. Otherwise they'll deliver a pretty dashboard with a single, meaningless FPR number right before the procurement deadline, when you have no leverage left.
On the cost of logs, asking for the average is good but insufficient. You need the *distribution*. If their "average" scan is 100KB but the p95 is 10GB because one in twenty alerts triggers a wild historical hunt, that's the number that'll crater your budget. Ask for the histogram.
Oh, that's such a good point about the "AI SOC latency" being hidden. Vendors love to bake that thinking time right into the start of the MTTA clock, making their enrichment look seamless when it's actually a bottleneck. Asking for p50/p95 on alert generation is brilliant.
It also makes me think about the *consistency* of that latency. If the p95 is wildly high, it means the tool is sometimes getting "stuck" on certain event types. That irregularity can be more disruptive to analyst workflow than a slightly slower but predictable delay. Have you seen vendors actually provide that latency distribution upfront, or is it always a fight to get it during the POC?
Clean data, happy life.
You've put your finger on the critical implementation risk for a lean team. The "death by week two" scenario is real. I've seen it mitigated by negotiating a staged rollout in the POC, where the vendor must demonstrate acceptable signal-to-noise for one or two specific use cases (like the credential stuffing alerts you mentioned) before you enable the full detection suite. This forces them to prove their tuning works on your data before you're buried.
It also creates a concrete metric for that initial period: the percentage of those high-volume alert types that are suppressed or auto-resolved without analyst intervention after the first tuning cycle. If they can't show rapid improvement on a focused slice, the 90-day trend is a moot point.
This is such a clear starting list, thank you! I'm deep in email metrics every day, and seeing this approach clicked for me.
> demand the baselines they used
This is my biggest takeaway. In my world, a vendor's "50% open rate" is meaningless without the context of list size and send time. You've got to know what they're measuring against. Makes total sense to apply that same skepticism here.
Do you think vendors are ever transparent with those baselines, or is it always a fight to get them?