Agree completely on the enrichment approach. That audit trail is crucial not just for AI errors, but for compliance during an audit. An alert that was enriched and hidden still exists for a forensic timeline.
Your non-production tag check is a smart, low-dependency method. The CMDB dependency risk is real; we've seen alerts become unmanaged due to stale data. One caveat: ensure your tagging logic is applied consistently. We had a scenario where a "dev" subscription briefly hosted a production data replica, which would have been incorrectly demoted. A secondary check for sensitive data classifications can mitigate that.
Buy once, cry once.
Yeah, that's a really good point about the AI adding its own noise. It's like adding a new alert type you have to watch.
> define what a 'high-sev case' even is
This part got me thinking. If you're building a whole new policy stack just for the AI, isn't that the same work you'd do to tune the original rules better in the first place? Feels like extra steps.
The circular logic with the 24-hour hold triggering is a bit spooky. How do you even start tuning the AI if its own decisions are part of the trigger?
You're right, it absolutely becomes a new alert type to manage. That's the hidden cost.
> isn't that the same work you'd do to tune the original rules better?
That's the key insight. It often is the same work, just shifted to a new, less transparent layer. The danger is teams start to see the AI score as an objective truth, when it's really just another rule set that needs its own tuning and maintenance.
The circular logic problem you mentioned is the real kicker. It creates a feedback loop that's incredibly hard to debug. You end up tuning based on symptoms, not root causes.
~Harry
Your segmentation analogy is excellent, and it directly informs a methodological approach to rule tuning. The consensus on modifying rule logic over thresholds is correct, but I'd formalize it by emphasizing the need for a causal structure in your conditions.
For instance, a rule demotion should be contingent on a well-defined, observable state that *explains* the lower risk. A "non-production" tag is a good start, but it's a proxy. A stronger logic clause would check for the conjunction of that tag *and* the absence of sensitive data classifications or crown jewel access patterns, which are more direct causes of business impact. This moves you from correlation to a form of causal filtering.
Regarding the AI Engine, the enrichment strategy discussed is operationally safer, but it's crucial to treat the AI score as an observed covariate, not a ground truth. If you use it to tag, you must periodically audit the `AI_Low_Confidence_Indicator` tag against eventual case outcomes to measure its precision. Otherwise, you're just adding a noisy, opaque feature to your alert metadata.
Nullius in verba
That's a great comparison, it really does feel like cleaning a messy email list. I'm new to LogRhythm and thinking about this from my CRM background too.
>Rule Tuning
Everyone here says to modify the logic, not just thresholds. Makes sense, like segmenting based on customer behavior instead of just an engagement score. But what's the first step? Do you start by looking at the lowest severity alerts that fire most often, or do you pick one type of alert that's most annoying to the team? How do you decide which rule to tune first?
And for the AI Engine, everyone says to use it to enrich, not filter. That seems smart for an audit trail. But doesn't that still leave you with the same number of alerts in your system, just tagged? You'd still need a separate dashboard filter to hide them, right?
Treating the risk score as a mutable state with a decay function is a sophisticated approach, and I appreciate you calling out the crucial reset condition for novel events. It formalizes the "crying wolf" concept.
Your method introduces a temporal dimension to risk that static rules lack. However, it creates a secondary data lifecycle you now have to manage: the decay schedule itself. We've seen similar models drift if the underlying asset context changes silently, like a developer machine being reassigned to a production support role but retaining its old event history.
It also raises an operational question about the 30-day rolling override. Do you have a mechanism to audit which asset-event pairs are currently suppressed by this system? If an incident occurs, you'd need to quickly determine if an alert was capped by this temporal watchlist versus the core AI logic.
brianh
That marketing segmentation analogy really clicks for me. I come from ERP and inventory management, where a bad filter can mean missing a stock-out just as easily as a security alert filter can miss a real threat. Your questions about step-by-step rule tuning are exactly where I've been focusing.
On choosing which rule to tune first, I don't think you should start with the most frequent or the most annoying. That can lead you down a rabbit hole. Instead, I'd map the alerts to a simple business impact matrix, like you would with supply chain disruptions. Which alerts, if missed, would actually halt a production line or breach a compliance rule? Tune the ones with high volume but provably low business impact first. It's a slower start, but it prevents you from accidentally silencing something that matters.
Your point about enrichment still leaving the same number of alerts is spot on. It feels like creating a separate "holding warehouse" in the system for low-priority items. You still have to manage that warehouse, and you need a reliable process to audit it. How often does your team actually review the filtered dashboard to validate the AI's low-confidence tags? If you never look, the audit trail is just theoretical.
The business impact matrix is a solid approach, but it requires a reliable asset-to-business-process mapping that many shops don't have. In the interim, I've had success starting with frequency plus a simple 'blast radius' metric. An alert firing 1000 times a day from a single test workstation is a safer first target than one firing 50 times from random nodes across production.
On the separate dashboard for enriched alerts, you're right to question the review cycle. We schedule a weekly Spark job that samples the low-confidence enrichments and pushes a random 1% back into the main queue. If the engine is working, none of those should escalate. It's a lightweight validation that the 'warehouse' isn't filling up with false positives.
Your martech approach is perfect for this. Tuning rules is exactly like building suppression lists, but for log sources.
>modify the rule logic itself
This is non-negotiable. Changing thresholds just moves the noise up and down. You need to add context as a condition. For example, add a clause that checks if the source IP is from your corporate VPN range before escalating an "impossible travel" alert from a managed workstation. That's your segmentation.
On the AI Engine: use it for enrichment only, never auto-suppression. Tag low-confidence alerts with something like "AI_LowRisk" and send them to a separate queue. Then, build a separate dashboard for that queue and review it weekly. If the AI is accurate, nothing in that queue should ever need to be pulled back. It keeps the audit trail intact while clearing your main console.
—hd
The manual override queue for specific assets is a pragmatic safety valve, and the 72-hour exemption window is a smart constraint. However, I've found that these manual channels can develop silent dependencies if not monitored.
The operational risk isn't the toil, but the potential for these exemptions to become permanent through inertia. We implemented a similar process but tied each exemption ticket to a mandatory review task that pops up in Jira 12 hours before expiry. Without that forced review, we discovered several servers had effectively been on a permanent "allow list" because the original context was forgotten. The system prevented rollback, but it also created a shadow policy. Did you run into any similar drift with your 72-hour rule?
Data over dogma
The manual overrides always become permanent. The Jira reminder is a good patch, but it doesn't fix the incentive problem.
The team who requests the override has zero motivation to remove it later. Their goal is to make their own noise go away. Once granted, that alert is now "someone else's problem." The review ticket just becomes an auto-close chore.
We attached a cost. Literally. We made the requesting team's cost center absorb the prorated monthly cost of the manual review and exemption management. Override requests dropped by 80%, and the ones that remained had a real business case. Suddenly, teams were eager to fix the root cause and turn the exemption off.
cost_observer_42
Love the maintenance window idea. We do something similar in Datadog with downtimes for scheduled jobs, but tagging those events with a `scheduled_task:true` property and adding that as an exclusion filter in the monitor logic was the real game changer.
Your watchlist approach is smart. We use tags for that, like `env:staging` or `team:dev`, and then our monitors have a clause like `env:production` to limit scope. The recurrence filter for multiple source IPs is a solid tip too - we use that for login alerts, but also for API error bursts. If it's all coming from one client IP, it's probably their bug, not ours. Saves a ton of noise.
Dashboards or it didn't happen.
Your segmentation analogy is spot on. For rule tuning in LogRhythm, modifying the rule logic itself, not just thresholds, is the only scalable approach. A threshold change just shifts the baseline noise floor. You need to add qualifying context.
For a concrete step, start by analyzing the 'AIE Offense' report for high-volume, low-severity offenses. Look at the raw log source data feeding those rules. A common example is the 'Failed Logon' rule; instead of just raising the count threshold, add a condition that excludes source IPs from your automated deployment or patch management systems. You segment the noise out at the source.
On the AI Engine, treat it as a classifier, not a filter. Configure it to add a 'confidence_score' meta-field. Then, in your dashboard and escalation workflows, you can filter where `confidence_score < 0.7`. This gives you a clear, auditable separation. The key is routing those low-confidence alerts to a separate review queue with its own SLA, not the primary console. This prevents the audit trail from being broken while still clearing the operational view.
--perf
The 72-hour case history check is critical. We've seen it catch lateral movement that would've been missed after an initial benign alert was demoted.
The AI Engine as a primary filter creates a single point of failure. Using its output as one enrichment field in a secondary rule is the right way. We pipe that `confidence_score` into a separate correlation rule that also requires at least two other independent indicators before downgrading severity. It's slower but avoids the black-box problem.
Your point about the AI both flagging and suppressing the same event is exactly why we avoid letting it set a status directly. It can only append metadata.
I'd zero in on the 'Failed Logon' rule example you and user112 touched on, as it's the quintessential noise generator. The key step after adding source IP exclusions is to verify those exclusions haven't created a blind spot. You need a control mechanism.
We run a scheduled report that compares failed logon events *from* the excluded source IPs (like patch systems) against successful logons *to* the same target systems from any other IP within a 10-minute window. The correlation is rare, but if it fires, it flags a potential credential stuffing attack using your automation infrastructure as a cover. This turns a static suppression into an active detection rule for a more sophisticated attack pattern.
On the AI Engine, I strongly advise against using its built-in risk score for any automated action, including demotion. We configure it to output a `predicted_class` field (e.g., "automated_traffic", "user_error", "suspicious"). We then feed that field into a separate, simple correlation rule that requires *another* independent signal, like a non-standard user agent or a geolocation mismatch, before it downgrades the alert to 'informational'. This prevents the AI from being both the accuser and the judge on the same event, which is a critical audit failure.