That's a huge point. We're implementing something similar, but the validation piece is scary. We're pulling from our CRM's asset inventory for the "valid, recent logon" check.
What happens when the validation step itself produces false positives? Say a critical server is offline for patching and fails the check. Do you have a mechanism to whitelist specific assets from validation, or do you rely on the rollback to handle it?
Totally feel you on the validation fear. That's where we had to build a manual bypass channel, essentially an "override queue."
When validation fails for a critical asset like a patched server, it doesn't roll back the whole list. Instead, it flags the specific asset and dumps it to a Slack channel for our security ops lead. They can manually approve a 72-hour exemption for that asset in the watchlist. It's a small, controlled manual step that prevents a full-system rollback over one noisy item.
It adds a tiny bit of toil, but it's way safer than letting validation blind us or constantly reverting the entire automation. Have you considered a similar, limited-exception lane?
That's a smart approach to handling those edge cases. The 72-hour exemption window is a good balance, giving a safety net without creating permanent blind spots.
My one caveat would be to make sure that Slack channel is highly curated and actionable. We tried a similar "override queue" in Teams, but it quickly got flooded and became background noise. We had to enforce that it only accepts posts from the automated system, not general chatter, to maintain its urgency.
Have you had any issues with alert fatigue in that specific channel?
The Teams channel turning into background noise is the inevitable end state for any 'exception lane'. You're right to lock it down, but the real test is when a critical asset gets flagged during a major incident and that single Slack message gets buried in fifty others about the same fire.
We made the same mistake early on by letting the channel accept manual 'urgent' requests from engineers. It became a dumping ground for "just this once" pleas that weren't exceptions at all. Your curation is the only defense, but it's a constant battle against entropy.
What's your fallback when the ops lead is on vacation and an exemption request times out? Do you let it fail closed, or does someone else get temporary keys to the kingdom? That's where these systems usually break.
Trust but verify.
>Which specific rule parameters or conditions have you found most effective for demoting or suppressing common false positives or low-risk events?
Thresholds are a blunt instrument. You'll get more mileage by modifying the logic to check for context. For example, a rule generating alerts for suspicious PowerShell execution is pure noise if it fires on your security team's jump boxes. We added a condition to first check if the source host was on a designated analyst workstation list. If yes, we drop the severity to "informational" and route it to a log-only case.
For the AI Engine, don't just let it score things. Use its low-risk classifications to feed a suppression rule. We have a rule that says: if the AI Engine tags an event as 'low risk' AND the asset hasn't been tagged in a high-severity case in the last 30 days, then demote the alert and hold it for 24 hours. If nothing else fires on that asset in that window, the alert auto-closes.
It's a step-by-step filter, not a one-time fix.
Run it yourself.
That AI filter sounds like you're just adding another layer of complexity that needs tuning. You've traded a noisy alert for a new one when the AI guesses wrong.
Now you've got to monitor and validate the AI's classifications, and define what a 'high-sev case' even is. That's a whole new policy stack.
Your 24-hour hold is smart, but what triggers it? The same AI you're questioning? Feels circular.
Keep it simple
Your 30-day log review is solid work, but quarterly tuning is too infrequent. That benign process list rots fast.
>tagged them with a "benign process" tag
This creates a permanent blind spot. What if one of those known servers gets compromised tomorrow? Your suppression list just silenced a real alert.
Better to expire those tags automatically after 30 days, forcing a review. A static list isn't segmentation, it's burial.
Simplicity is the ultimate sophistication
Your step-by-step filter is the right idea, but that 24-hour hold window is a gamble on timing. An attacker who establishes a foothold on a suppressed asset could easily be active within that window, and you've just delayed the alert while they move laterally. We use a similar model, but we pair it with an immediate, separate notification to a dedicated analyst review queue. The main alert is demoted, but a human still sees the event in under five minutes.
The bigger issue with whitelists like your analyst workstation list is drift. How do you maintain that list? We got burned when a "security workstation" was re-imaged and changed its hostname, falling off the list and causing a sudden flood of alerts we thought were suppressed. Now our suppression lists are built dynamically from a CMDB tag, which at least ties the logic to a system of record, not a manually managed text file. It's still not perfect, but it fails a bit more gracefully.
Also, what happens when an analyst's workstation itself is compromised? Your informational-only routing just created a silent blind spot for a high-value target.
Your marketing automation mindset is perfect for this. Tuning rules is similar to segmenting a campaign - you need dynamic lists, not static ones.
For rule tuning, don't just adjust thresholds. Modify the logic to include dynamic context. For example, we check if the triggering host is tagged as a "developer workstation" in our CMDB, then demote the alert severity. This prevents the drift problem when hostnames change.
On the AI Engine, we use it to flag low-risk events, but we pair it with a short 5-minute review queue for an analyst, not a 24-hour hold. That way we still catch the signal if something starts moving fast.
Automate the boring stuff.
Your marketing analogy hits the nail on the head. Static lists are the death of this process.
You asked for specifics on rule tuning. The most effective condition we added wasn't a threshold, it was an exclusion based on **recent, correlated high-severity events**. In LogRhythm, build a rule that first checks if the source entity has been involved in a confirmed incident (Case Status = Closed/Confirmed) within the last, say, 7 days. If it has, the rule lets the alert through at its original severity. If not, it applies your demotion logic. This stops you from blindly suppressing activity on a potentially compromised asset.
Regarding the AI Engine, we use it as a *weighting factor*, not a gatekeeper. Our primary rules run and generate an initial severity. A secondary, separate AI-driven rule then analyzes the same event. If the AI scores it as low risk *and* the entity's risk score is below our dynamic threshold (pulled from our CMDB, not a static list), it adds a tag like "AI_Low_Concern" and routes it to a daily digest queue instead of the main console. This way the AI can be wrong without completely blinding us.
The step everyone misses is the feedback loop. Every alert in that daily digest gets a manual "Was this useful?" flag from the analyst who reviews it. Those "No" votes are what we use to retrain and adjust the AI model parameters quarterly, not some arbitrary calendar date. Your lists have to be as dynamic as your attacker's tactics.
You're approaching this with exactly the right mindset. Your comparison to cleaning an unengaged email list is apt, but the stakes are higher; you can't just move a potentially compromised host to a "suppression list" and call it a day.
You asked for specific, step-by-step tuning. For rule parameters, moving beyond static thresholds is key. The most effective condition we've implemented is a dynamic check against recent case history. Build a rule clause that queries whether the source entity has been associated with any confirmed, high-severity case in the last 72 hours. If it has, the alert bypasses all demotion logic and fires at full severity. This prevents you from automatically silencing activity on an asset that might already be under investigation.
On the AI Engine, I disagree with using it as a primary filter. We treat its low-risk classification as merely one data point among many in a secondary correlation rule. The primary detection logic runs independently. The AI's score can then adjust severity, but never to "informational" without a human in the loop. This avoids the circular logic problem where the AI both flags and suppresses the same event.
Your watchlist idea has merit for known-good entities, but it must be built and maintained dynamically, sourced from a system like a CMDB, not a manual spreadsheet. Otherwise, you're just creating future noise when a server gets reprovisioned and falls off the list.
Your comparison to segmenting an email list is spot on. The dynamic context check others mentioned is key - treat your CMDB like a dynamic segment. A rule that checks if a source is tagged "non-prod" or "user workstation" before demoting an alert has cut our noise dramatically.
But I'd push back slightly on leaning too much on the AI Engine as a filter. In my experience, using it to *enrich* rather than *gate* is safer. Let your base rules fire, but have a secondary rule that consults the AI score and *adds* a tag like "AI-Low-Risk" to the alarm. That way the event is still logged and searchable, but your dashboard can filter on that tag to clear the view. It creates an audit trail if the AI gets it wrong.
Static watchlists are a maintenance nightmare. Can you build them from an external source, like your asset inventory, instead of managing them inside LogRhythm? That was a game-changer for us.
You've got a great starting mindset. Your rule tuning question is exactly where to focus.
>adjust thresholds, or modify the rule logic itself?
Always modify the logic. Thresholds just change volume; context changes meaning. We had huge success with a simple addition: before demoting any "suspicious login" alert, the rule now checks if the source IP is in a specific, dynamically updated geolocation block we define as "corporate travel hubs." If it is, severity drops. If not, it fires at full. This cut noise by 60% overnight because it accounted for real user behavior.
For the AI Engine, I'd caution against using it to suppress. We use it to *flag* instead. We have a rule that adds "AI-LOW-CONFIDENCE" as a tag to the alarm metadata if the score is below a threshold. The alert still appears in the main queue, but our dashboard view filters it out by default. This gives us an audit trail and the ability to quickly review if needed.
I'm curious, have you looked into building dynamic watchlists from your AD or CMDB yet? That's been the real game-changer for us, similar to keeping a marketing segment fresh.
Always testing.
Your marketing automation experience is a huge asset here. You're right to focus on rule logic over thresholds.
For your rule tuning question, the most impactful step is to add a condition that references your case management status. Build a clause that checks if the source entity is currently associated with an *open* case. If it is, bypass all suppression or demotion logic entirely. This ensures you aren't accidentally muting activity on a host that's already under investigation.
On the AI Engine and Watchlists, I'd advise against using them as primary filters. Instead, use the AI score as an enrichment field. Set up a secondary rule that appends a tag like `AI_Low_Confidence_Indicator` to the alarm metadata based on a score threshold. Your console can then filter views by that tag, but the raw alert remains in the log for audit. This gives you the noise reduction without creating a blind spot.
Static watchlists are a trap, as you know from managing contact lists. If you must use them, source them from a dynamic system like your CMDB, and build in a 30-day expiration for any entry to force periodic review.
—Anita
Your marketing segmentation mindset is a great fit. On rule tuning, I'd push back slightly on some of the dynamic list approaches only because they can fail silently if your CMDB feed breaks. We added a simple but effective condition to our demotion logic: check if the alerting entity is currently running any *non-production* workloads. In our case, we tag AWS accounts and Azure subscriptions. A rule that checks for a "sandbox" or "dev" tag before downgrading severity cut a huge chunk of noise with less dependency on a complex asset database.
For the AI Engine, I'm firmly in the "enrich, don't filter" camp. We have a rule that appends a custom field like `AI_Risk_Score: Low` if the engine's confidence is below our threshold. The alert still fires, but our dashboard rules hide anything with that tag from the primary view. It gives you a clean console but keeps the audit trail intact, which you'll want when the AI inevitably flags something wrong.