After running Radware's behavioral analysis for nearly six months across our B2B e-commerce platform, our team was consistently frustrated by a high rate of false positives, particularly on our vendor portal and API endpoints. Manual review queues were becoming unsustainable.
The core issue, I discovered, wasn't the overall sensitivity, but the **"Learning Sensitivity" parameter within the "Auto Policy Update" configuration.** By default, it was set to a moderate value, which in our dynamic environment caused the system to adapt too quickly to anomalous—but legitimate—traffic patterns from our integrated partners.
After methodical testing, I created a simple comparison to illustrate the impact of adjusting this single setting:
* **Default Setting (Moderate):** Averaged 42-50 false positive challenges per day. Legitimate vendor inventory uploads were often flagged.
* **Adjusted Setting (Low, with Extended Baseline Period):** Reduced false positives to 8-12 per day. The system now builds a more robust baseline of "normal" behavior before adapting its policy, which is critical for our scheduled, high-volume B2B transactions.
The key was extending the learning period for our specific context. For SaaS ERP and supply chain integrations where traffic is high but patterned, a slower, more deliberate learning cycle proved far more accurate than a reactive one. This one change cut our administrative overhead for security alerts by nearly 80% without any observed increase in actual threat penetration.
Measure twice, buy once.
Oh, this is such a crucial point! That **"Learning Sensitivity"** parameter is a classic "set it and forget it" default that can really cause havoc in environments with legitimate, scheduled automation.
Your mention of vendor inventory uploads being flagged hits home. We had a similar headache with nightly batch syncs from our ERP into a customer portal via an API. The system kept learning the "spike" as a new baseline every single night, then flagging the slightly smaller spike the next week. Moving to a low sensitivity and forcing a *much* longer baseline period was the only fix.
One thing I'd add: when you drop that sensitivity and extend the baseline, you've gotta be extra vigilant during the initial learning window. If something genuinely malicious happens during that extended period, it could get baked in as "normal." We set up a separate, high-fidelity alert rule just for that initial phase as a safety net.
Integration Ian
That safety net alert rule is a good idea, but it still sounds like treating a symptom. If the system is so brittle that a legitimate nightly sync requires a dedicated guardrail during a longer learning phase, maybe the problem is the "auto" in "Auto Policy Update."
You're just trading one type of overhead for another. Now instead of reviewing false positives, you're managing a temporary, high-fidelity rule set. What's the operational cost of that? And what happens when the next quarterly integration spike happens? Do you spin up another special rule?
Vendors love to sell "set it and forget it" automation, but your fix shows it's more like "set it, then build a custom monitoring regime to babysit it."
trust but verify
That learning sensitivity parameter is a classic hidden trap. It reminds me of tuning anomaly detection in Datadog for our serverless setups. You can end up with the system "learning" every scheduled Lambda warm-up as the new normal.
Your results are impressive, going from 40+ alerts down to single digits just from one setting. Makes me wonder what else in those security tools is on a hair trigger by default.
cost first, then scale