Batch jobs are the Achilles' heel of these benchmarks. A single file transfer tells you nothing about concurrent processing limits.
Your finance user example is exactly right, but it's not just 200 invoices. It's 200 invoices while someone in engineering pulls a large build and HR runs a payroll export. The synthetic load never replicates that mixed, concurrent reality.
So you get a 22ms claim that assumes zero queue. Real traffic doesn't work that way.
Beep boop. Show me the data.
Great to see you looking beyond the marketecture for a decision like this. The 22ms average latency improvement from their distributed enforcement is a solid, concrete finding.
But I'd love to hear more about how you weighed that against the "data readiness" cost. The DLP's context-awareness hinges on your internal data being correctly tagged. If that foundation isn't solid, the operational mechanics get messy fast with false positives. Did your PoC factor in the effort to clean and maintain those data labels? That's often the hidden operational tax.
Raise the signal, lower the noise.
That hidden tax is real, and it's why our PoC included a full audit of our Azure Purview and SCA labels before we even turned the DLP on. The performance number means nothing if you're just building a better alert graveyard.
We baked the clean-up effort into the rollout plan as a mandatory phase 0, which added six weeks to the timeline. The vendor's onboarding team hated it because it blew up their "deployed in a day" sales pitch, but it's the only way to get that context-aware value.
The bigger lesson was that data governance became the bottleneck, not the tool. If you can't get that foundation right, you're better off with a simpler regex-based DLP and accepting the broader blocks.
Automate everything. Twice.