Hot take: QRadar's 'AI' capabilities are just basic correlation with a fancy label.

I've spent the last six months evaluating SIEM platforms, with a deep dive into QRadar 7.5.0. The marketing push around its "AI-driven insights" is, frankly, misleading. After tearing apart the workflows and testing the "offenses," it's clear the core engine is still fundamentally rules-based correlation with statistical weighting slapped on top.

Here's what I mean. The "AI" label seems to be applied to two things:
* **Anomaly detection dashboards:** These are essentially rolling baselines of event counts. A spike in "Failed Logins" from a source gets flagged. This is simple statistical deviation, not machine learning understanding of context. You can replicate 80% of it with a custom rule and a time series chart.
* **Offense "confidence" scoring:** A high-confidence offense appears to just be one that hits on multiple correlation rules simultaneously. If a single source triggers "Malware C&C Beacon," "Unusual Outbound Traffic," and "Data Exfiltration Protocol" rules, the confidence is high. This is multi-condition correlation, not predictive AI.

I set up a test lab with simulated traffic. The so-called "machine learning" models required an impractical amount of "clean" baseline data to configure and still generated overwhelming false positives on any unusual but legitimate activity (e.g., a scheduled bulk data transfer). Tuning them was identical to tuning a traditional rule: adding exceptions and whitelists.

My benchmark shows QRadar's real strength is in its **correlation engine and app ecosystem**, not any genuine AI. For the price point, I expected more than what amounts to:
```sql
-- Simplified analogy of what's happening under the hood
SELECT source_ip, COUNT(*) as event_count
FROM events
WHERE log_source_type = 'Windows'
AND event_name = '4625' -- Failed Login
GROUP BY source_ip, HOUR(event_time)
HAVING event_count > (baseline_avg * 3)
```

Has anyone else done a technical tear-down and reached a different conclusion? I'm looking for reproducible evidence of true behavioral modeling, not just vendor demos showing the pre-tuned "Sample Offenses." What specific use cases have you found where QRadar's AI demonstrably outperformed a well-written correlation rule?