Alright, let's talk about Panther's new "auto-tuning" for alert thresholds. On paper, it's a classic vendor promise: set it and forget it, let the machine learning magic eliminate alert noise. I've heard that song before from a dozen other platforms.
My immediate question is: what's it actually tuning against? Historical data? If my baseline was noisy to begin with, won't it just learn to accept the noise? I ran a test on a common brute-force detection rule. After a week, the auto-tuning suggested a threshold that would have let through a handful of clearly malicious attempts last month. When I dug into the logs, it was because those attempts happened during a period of unusually high legitimate traffic—so the "tuning" just raised the bar for everyone.
And what's the cost? This isn't just a toggle. It's going to chew through more log data for the model, likely impacting your SIEM ingestion costs if you're on a usage-based plan. I haven't seen Panther explicitly call out those incremental compute costs yet. Is the ROI there if I'm now paying 5-10% more on my monthly bill to maybe reduce my tuning time by a few hours?
I'm also deeply skeptical of any "black box" adjustment to my security logic. Can I see a diff of what it changed and why? Or am I just supposed to trust that the algorithm knows better than my team's understanding of our own environment? Vendor lock-in isn't just about contracts; it's about becoming dependent on their proprietary logic you can't audit.
Would love to hear from others who've pushed this feature beyond a demo environment. What are you seeing in your actual alert volumes and, more importantly, in your true positive rate?
— skeptical but fair
Yeah, the historical data dependency is a massive gotcha. You're spot on about it potentially learning to accept noise.
I've seen similar behavior in auto-scaling for data pipelines. If your baseline includes a weird outlier event, the model will treat that as the new normal. It needs perfectly "clean" historical periods to learn from, which in security is almost a paradox.
On the cost, you're right to be wary. Ingesting all that extra log data for model training isn't free, and most platforms bury that in the fine print. I'd want to see a clear breakdown of what "auto-tuning" actually ingests versus my regular alerting before flipping that switch.
Data nerd out