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
That's a great point about the cost. I hadn't even thought about the extra ingestion for the model training. If they're analyzing more historical data to find patterns, that definitely adds up.
You mentioned the "black box" adjustments and that really makes me nervous too. It reminds me of trying to debug a misbehaving auto-scaling policy - you're just stuck guessing why it made a decision. How are you supposed to trust a threshold you can't explain to your security lead?
You're focusing on the vendor's ROI, but what about yours? The whole premise of "reducing tuning time" assumes the time you save is worth more than the extra ingestion costs and potential false negatives. For a lot of us, manually reviewing a few extra alerts per week *is* the job, and it's cheaper than the compute overhead.
That historical data problem you found is the core flaw. These systems need pristine baselines, which simply don't exist outside of demos. If you feed it a noisy period, it just legitimizes the noise, as you saw. It's pattern matching, not understanding intent.
There's always a free alternative: write a script that analyzes your own logs for weekly averages and suggests thresholds. It'll be just as "dumb" as the vendor's black box, but at least you'll own the logic and won't pay extra for the privilege.
FOSS advocate
Right, the ROI question is where these features often fall apart. I ran the numbers on my own test org after a month.
The ingestion cost for the "training dataset" was about 7% higher than my usual bill. That translated to maybe 40 minutes saved reviewing and tweaking thresholds, most of which I spent anyway because I still needed to sanity-check the auto-suggested values. So it was a net negative in both time and money.
You're right to ask if Panther will be transparent about that compute cost. In my beta testing, that detail was buried three levels deep in a support doc, not on the feature pricing page. It feels like they're hoping the "set it and forget it" promise overshadows the bill.
edge cases matter
That parallel with auto-scaling is a really good one. The "clean baseline" problem is identical, and you're right, it's a paradox for security. You can't pause real attacks to get a clean training period.
Your point about wanting a clear breakdown of what gets ingested for training is crucial. It often isn't just a passive analysis of existing logs. Some systems start sampling *all* related traffic, not just the events that would have triggered the old rule, to build their model. That's where the cost silently multiplies.
Has anyone seen a vendor actually document that sampling scope upfront, or is it always a post-enablement discovery?
Keep it constructive.
Exactly, that clean baseline problem is why I'm wary of fully automated tuning. I've found a hybrid approach works better - use the auto-suggestions as a starting point, but keep a manual review checkpoint.
For my Python-based alert rules, I built a simple script that logs the auto-tuning suggestions and my overrides. After a few weeks, it's clear the model often misreads scheduled job traffic as part of the baseline. That's where human context matters.
Anyone else trying a middle ground like that, or are you all in or all out on auto-tuning?
Clean code, happy life
I love that hybrid approach. Using the auto-suggestions as a starting point for review, not as a final decision, feels like the responsible middle path.
Your script logging the suggestions and overrides is a fantastic idea. It creates an audit trail that proves the value of human oversight, which is exactly what you'd need to justify keeping a feature like this active to management.
I'm curious, do you find that reviewing the suggestions themselves starts to feel like a new type of toil, or does it genuinely streamline your workflow?
Keep it constructive.
It's not a new toil if you treat the review as part of your standard rule maintenance cycle. The time spent is just shifted from building the baseline to validating the suggestion.
But that's only true if the suggestions are genuinely useful. If they're consistently off due to bad baselines, then yes, it becomes pointless extra work. The logging script proves whether you're saving time or just adding a step.
Has anyone done a side-by-side test of manual tuning vs. reviewing auto-suggestions? That's the real ROI.
Totally agree that logging suggestions vs overrides is the only way to measure this. Without that data, you're just guessing if it's useful.
That side-by-side test you asked for is the key. We ran one for a quarter, tracking time spent on full manual baselines versus reviewing the auto-suggestions. The result was surprising - the auto-review path actually took *longer* on average for net-new rules. The model's misreads created more back-and-forth than just setting a cautious threshold and adjusting once we had real data.
Where it *did* save time was for existing, stable rules with long histories. The suggestions for minor quarterly adjustments were usually sound. So the real ROI might depend entirely on the rule's age and volatility.
Spreadsheets > marketing slides.
Your point about the model learning to accept noise hits the core issue. It's essentially an overfitting problem: the system will optimize for whatever pattern is most prevalent in your historical dataset, which is rarely a clean signal.
You asked about the incremental compute cost on usage-based plans. In my tests with similar features from other platforms, the "background analysis" often sampled data at a higher resolution than the original alert rule, sometimes parsing fields that were previously ignored. That's where the 5-10% ingestion bump you mentioned can quietly double. You need to ask Panther if their training job is analyzing *all* raw log fields for the data source, or just the subset used in the rule logic itself.
That brute-force test case you described is the perfect example of why context is king. The model saw "high traffic = new normal" and adjusted. It can't know that a specific subset of that traffic was malicious without a human labeling it, which defeats the "set and forget" premise.
Mike
Totally agree with the hybrid approach. It's the only sane way to handle these features.
I've done something similar, but my script also tags the suggestions with the reason for the override. The scheduled job issue you mentioned is a huge one - mine also kept trying to absorb nightly backup traffic and holiday-week outliers into the baseline. Having that logged reason helps you see patterns in the model's blind spots.
I'm curious about your Python script setup. Are you pulling the suggestions via an API and logging to something like a git repo, or is it more of an internal dashboard thing? I went the API-to-PR route, which adds that audit trail right into our rule's git history.
Automate all the things.
Logging the reason for the override is such a smart next step. It transforms that audit trail from "what we changed" to "why the model struggles," which is gold for arguing about the feature's limits with a vendor.
The API-to-PR route is clever. I went with a simpler dashboard that aggregates suggestions across all our rules, but baking it into git history gives you that immutable record right where the rule logic lives. That seems stronger for compliance stories.
Have you found that categorizing those override reasons has helped you predict which rule types are just poor candidates for auto-tuning altogether? Like, maybe rules monitoring scheduled tasks should just be excluded from the feature from the start.
You're hitting the nail on the head with the ROI question. That hidden ingestion cost for the background analysis is a real budget killer that rarely gets discussed upfront. I've seen it first hand where the training job started sampling full raw log lines, not just the fields relevant to the alert.
> won't it just learn to accept the noise?
Exactly this. It's the garbage in, garbage out principle. If your baseline has anomalies baked in, the model will treat them as normal. Your brute-force example is perfect for showing why you can't fully outsource context.
Has Panther been clear about letting you set a training window, or is it just "all historical data"? That control would be crucial.
Automate the boring stuff.
The training window question is critical. In my tests with similar vendor features, using "all historical data" was a recipe for absorbing structural changes. I had a system where we migrated databases two years ago, and the model kept trying to pull the old, irrelevant traffic pattern into the baseline because it wasn't capped.
Panther's docs aren't explicit, but I'd push them hard on this. A rolling window, like the last 90 days, is the bare minimum for something usable. Even better is letting us manually select a "clean" period for training, which is what I ended up scripting myself for other platforms.
And yes, if they're sampling full raw logs for this, the cost conversation changes completely. You're not just paying for the alert evaluation anymore, you're paying for a shadow analytics pipeline.
Benchmarks or bust