Hi everyone. I've been trialing Radware for a few weeks now, and I just finished building a small dashboard to compare traffic patterns. I'm still learning, but I wanted to share what I'm seeing.
The before/after difference in attack patterns is really interesting. Before, we'd see a lot more basic volumetric attacks hitting our test servers directly. Now, the dashboard shows those are being absorbed upstream. We're seeing more sophisticated, low-and-slow stuff getting flagged at the app layer instead. Has anyone else built similar monitoring? I'd love to know what metrics you found most useful to track 😊
Absorbed upstream sounds nice until you're the one on-call when it absorbs legitimate traffic right along with the garbage. That "sophisticated" app-layer stuff you're seeing flagged is often just slightly weird organic traffic.
The metric I found most useful wasn't the blocked requests count. It was the 95th percentile latency for your users in the next region over when the WAF kicks into "scrutiny mode." If you haven't got that in your dashboard, you're only seeing half the picture. Maybe the quiet half.
Latency is the real tax you pay for security. Good call.
Add regional service error rates to your 95th percentile latency view. That scrutiny mode can fail requests silently while making others slower. If your error rate spikes in a region when p95 goes up, you've found your problem.
"Absorbed upstream" just means you're trusting someone else's black box with your traffic. You've traded visible noise for invisible filtering decisions.
What's your dashboard's false positive rate for that "sophisticated" app-layer stuff? That's the only metric that matters. It's probably higher than you think, and you're just not seeing the blocked legitimate requests.
If it ain't broke, don't 'upgrade' it.
That's a fair point about the invisible filtering decisions. The false positive rate is critical, but it's also incredibly hard to measure accurately on the app layer.
You can't just look at the "blocked" count. We log every flagged request that hits the learning/observation mode and sample-review them. The real metric is how many of those *would have been blocked* without human tuning. That number is rarely zero, but it's also not the tidal wave some expect.
It comes down to the feedback loop. If you're not constantly reviewing those flagged-but-allowed requests and feeding that back into the policy, then yeah, you're just flying blind in a different way.
Ship fast, measure faster.