Hey everyone, been using Panther for a few months now to handle our cloud security log analysis. I mostly like it, but I just got the email about their new "AI-powered" alerting features.
I'm trying to cut through the marketing speak. They say the AI can reduce false positives and correlate events, but I haven't seen concrete examples of *how*. In my current setup, I'm writing Python-based detection rules, and false positives are a constant battle. Is this "AI" just a fancy filter on top of existing rules, or is it actually doing something novel?
For instance, if I have a rule that triggers on an AWS `ConsoleLogin` from a new region, the old way I'd add exceptions manually. Would this new system automatically learn my team's normal access patterns? And if so, how do I trust it? As a newcomer to this level of tooling, I'm worried about it becoming a "black box."
```python
# Example of a basic rule I'm currently managing
def rule(event):
# Alert on ConsoleLogin from a new region
return (event.get('eventType') == 'AwsConsoleSignIn' and
event.get('userIdentity', {}).get('sessionContext', {}) is None)
```
Did anyone else get a chance to test this in the beta? I'm curious about the actual workflow. Do you now have to review "AI suggestions" before an alert fires, or does it auto-suppress? How much configuration did it need?
Maybe I'm just skeptical because I've seen "AI" slapped on so many features lately. I'd love to hear from anyone who has moved from traditional rules to this new system. What's the real-world improvement, and what are the new pitfalls?
Learning by breaking
Probably a bit of both. Their blog post mentions using anomaly detection models on historical event data. For your ConsoleLogin example, it would try to build a baseline for each user's typical access times, source IPs, and regions. Logins outside that baseline get a higher severity score.
The trust issue is valid. You'll need to audit its "decisions" for a while. Check if it starts filtering out legitimate new access from a contractor or a traveling engineer. The cost of missing a real threat is higher than tuning a few Python rules.
Ask for their model's precision/recall metrics from the beta. If they can't provide them, it's just a fancy filter.
cost per transaction is the only metric