Alright, fellow automation wranglers, I've been wrestling with LogRhythm's AI Engine for a few months now. The potential is incredible—automating threat detection and taking some load off the SOC is the dream. But man, the road to that dream is paved with false positives. I feel like I'm constantly tuning out noise instead of finding signals.
I've got a decent handle on the basics: starting with a focused data set, using known-good baselines, and applying the engine to low-risk use cases first. But I'm hitting a wall when it comes to scaling that initial success. How do you systematically *train* this thing without creating a full-time job for yourself managing alerts?
Here’s my current approach, but I’d love to compare notes:
- **Phased Rollout by Log Source**: Started with Windows Event Logs (auth failures) before touching network data. Less chaotic.
- **Tagging is King**: I’m obsessive about tagging everything in the AI Engine's training interface. "False Positive – Benign Scan," "True Positive – Confirmed Brute Force." This feels crucial.
- **Weekly Review Sprints**: Dedicating 90 minutes every Monday to review the previous week's AI-generated alarms, adjusting models, and adding to the knowledge base.
Where I'm struggling:
- The feedback loop feels slow. How granular do you get with your "normal" behavior models?
- Any clever tricks for using LogRhythm's own case management data to auto-tag or inform the AI models?
- At what point did you feel confident enough to let the AI Engine make automated recommendations (like adding to watchlists)?
Specifically, for those who've integrated this with a marketing or corporate IT environment (like tracking suspicious asset access), how did you balance the unique "normal" of a marketing team (crazy download volumes, odd-hour CMS logins) with actual risk?
I’m hoping we can share some concrete workflow steps or even "automation recipes" for managing the training process itself. What’s your review cycle? What metrics do you watch to know you're improving?
automate or die
1. Security lead at a 600-person SaaS shop. We run Prometheus/Loki/Jaeger for infra/app observability, LogRhythm for SIEM, and I've had the AI Engine in production for about a year, mainly on cloud auth and endpoint logs.
2. Core comparison for training the AI Engine without burning out:
- **Training Data Scope**: Start with a single, high-fidelity log source. We used Azure AD sign-in logs exclusively for the first 3 months. Volume was about 50k events/day, manageable for manual review.
- **Tagging Discipline**: Your tagging is correct. We enforce a rule: every AI-generated alarm gets a tag before closure. Critical tags are `FP:ExpectedBehavior` and `TP:ConfirmedCompromise`. We have 12 defined tags; more than 20 becomes unmanageable.
- **Review Cycle Cadence**: Weekly reviews are too slow early on. We did daily 30-minute sessions for the first month. After false positives dropped below 15%, we moved to bi-weekly. Our threshold for "tuned" is <10% FP rate for a given model.
- **Model Adjustment Pace**: Never adjust more than two detection models per review session. Changing more introduces cascading noise. Our biggest win was increasing the "deviation threshold" parameter on our brute-force model from the default 2.5 to 3.8, which cut FPs by 60% without missing true events.
3. My pick: Stick with your phased rollout but compress the review cycle initially. The AI Engine works if you treat it like a junior analyst: require supervised output daily before letting it run semi-autonomous. For a clean call, tell us your average daily alarm volume from it right now and which log source is your next planned phase.
Metrics don't lie.
Great points on tagging and review cadence! Your `FP:ExpectedBehavior` tag is spot on - that's exactly how you feed the engine what "normal" looks like.
One thing I'd add from our AWS IAM logs journey: we created a parallel `FP:AuthorizedException` tag for known, sanctioned weirdness. Think break-glass admin logins or those quarterly vendor data pulls. Tagging those separately kept our baseline training cleaner, because the engine learned they were *approved* outliers, not just noise.
Your <10% FP threshold is solid. We found that pushing below 5% for cloud logs often meant we were missing real, low-and-slow anomalies. Sometimes a little strategic noise is better than total silence.
security by default
That split between ExpectedBehavior and AuthorizedException is a really smart way to handle the training data. We found a similar need when training on Okta logs.
Our twist was adding a third tag: `FP:ScheduledTask`. This captured automated service account activity that was technically authorized but had a different pattern than human break-glass access. Separating 'planned weird' from 'emergency weird' gave the model better signals.
And totally agree on the 5% threshold being a false idol. Chasing that last few percent of FPs often meant adding so many exclusions that we'd blind the engine to new attack vectors. Our sweet spot was around 8%.