Unpopular opinion: Their 'AI' labeling is just fancy rate-limiting.

I've been evaluating Radware's Cloud WAF and Bot Manager for a potential deployment, and after a deep dive into their documentation and running some controlled load tests, I've reached a conclusion I haven't seen widely discussed. Their heavy marketing around "AI-driven" and "Intent-Based" attack detection is, in my professional assessment, largely a rebranding of sophisticated, context-aware rate-limiting and challenge mechanisms. It's not the autonomous, learning-based system the terminology implies.

Let me be specific. I set up a test endpoint behind their service and simulated several traffic patterns:
* Low-and-slow credential stuffing from distributed IPs.
* A burst of requests for a specific, newly listed product SKU.
* Scraping attempts with realistic but spoofed browser headers.

The "AI" model's primary action, in over 90% of triggered cases, was to issue a challenge (either a JavaScript or cookie challenge) or to throttle the session. When I analyzed the logic, the decision points consistently mapped to thresholds and correlations that are, fundamentally, programmable rules.

Here's a simplified abstraction of what I believe their "AI" label is often wrapping:

```yaml
# Not actual Radware config, but a conceptual model their system likely implements.
detection_rule:
name: "Suspicious_Scraper_AI"
inputs:
- requests_per_session
- endpoint_volatility_score
- failed_challenge_ratio
- header_anomaly_index
decision_tree:
if header_anomaly_index > threshold_alpha:
action: "challenge"
elif requests_per_session > threshold_beta and endpoint_volatility_score > threshold_gamma:
action: "throttle_to_rate_limit"
elif failed_challenge_ratio > threshold_delta:
action: "block_session"
label: "AI_Model_Intent_23"
```

My issue isn't that the system is ineffective—when tuned, it works adequately. My issue is the **labeling**. In our field, "AI" has come to imply adaptive learning, model retraining on your unique traffic, and predictive mitigation. What Radware is delivering here appears to be a well-engineered, multi-dimensional rate-limiting and challenge engine that uses statistical baselines. This has significant implications:

* **Cost Justification:** They command a premium price for "AI." Are you paying for the buzzword?
* **Expectation Setting:** If you expect it to learn your novel, complex attack patterns without extensive manual tuning and rule creation, you will be disappointed.
* **Operational Overhead:** The "black box" nature of their "AI" decisions makes troubleshooting more opaque than a clear rule-based system. Support often falls back to "the model decided," which is not a root cause.

I want to see concrete, auditable evidence of what makes it AI. Can anyone from Radware or a user with deeper access share:
* The specific machine learning models in use (e.g., LSTM, clustering algos)?
* Where the training data comes from and the retraining frequency?
* A single example of a mitigation action that a well-crafted, traditional rule *could not* have achieved?

Without this, I'm filing their "AI" under marketing gloss for deterministic algorithms. It's a competent product, but let's call the technology what it is.

—davidr