Got my hands on the new ML detection module in their Edge Security package. Early testing shows a 40% reduction in false positives on our API endpoints, but latency took a hit.
Ran a simple benchmark against a known-good traffic profile mixed with some low-and-slow attack patterns.
* ML module caught 99% of the malicious patterns Cloud Managed Rules missed.
* However, median request latency increased by 85ms during the learning phase.
* After the initial 24-hour "burn-in," the latency spike dropped to ~15ms.
My test config:
```json
{
"security_controls": {
"ml_detection": {
"api_endpoints": true,
"learning_mode": false,
"aggressiveness": "high"
}
}
}
```
Biggest win was tuning out false positives on our GraphQL mutations. The old WAF rules were a nightmare.
Anyone else running this? Curious about your real-world latency numbers after the learning period. Is the trade-off worth it for your threat profile?
Benchmarks or bust.
That's a huge reduction in false positives! Our team has been talking about this module too. You mentioned running it with learning_mode off and aggressiveness high from the start - our security lead is worried that might block legitimate traffic while it's figuring things out.
We run a lot of GraphQL as well, and the regular WAF rules are, well, you said it. Did you see many legitimate user mutations get flagged in that initial 24 hours before the burn-in finished? The 85ms hit is steep, but if it settles down, 15ms for that accuracy seems kinda worth it.
I'm still trying to understand the threat profile part - when you say it's worth it for your profile, do you mean mostly for catching those low-and-slow attacks that signature rules miss?
That 85ms during the learning phase would make my SRE team wince, but if it stabilizes at ~15ms, that's a trade-off I'd seriously consider. We've been chasing those low-and-slow bot attempts for months.
Running it with `learning_mode: false` right out of the gate is brave! We're leaning towards letting it soak in monitoring-only for a week on our staging environment first, just to see what it tags. I'd be worried about flagging a legitimate, but slightly odd, user journey as malicious during that initial period.
The false positive reduction on GraphQL is the real story here, though. Our old ruleset flagged so many perfectly normal nested queries that our analytics were a mess. If this cleans that up, the latency trade is a no-brainer for us. Have you checked if the latency bump is uniform, or does it spike more on specific mutation types?
I totally get your SRE team's concern about that initial latency hit - 85ms would set off alarms for us too. But that stable ~15ms afterwards is exactly the kind of predictable trade-off I can advocate for with our product teams.
Your staging plan is smart. We actually ran it in monitoring-only on a subset of our production traffic for 48 hours first, specifically watching those "slightly odd" user journeys. It caught a few edge cases in our checkout flow that looked bot-like but were just... enthusiastic users on slow connections. Tuning those out before enabling blocking saved us some headache.
About the latency spikes - in our tests, complex mutations with deeply nested objects saw the highest increase during learning, sometimes double that 85ms median. Simple queries barely budged. After burn-in, it all leveled out. Might be worth checking your staging data for that pattern.