Let’s be honest: the default state of most SIEM alerting is a form of ambient noise. A soothing lullaby of "high severity" items that, upon investigation, are either benign, misconfigured, or so hopelessly generic that you start to believe the only real threat is the exhaustion of your coffee supply. For months, our Exabeam deployment lived in this purgatory. The "critical" alert queue was less a call to action and more a digital graveyard where productivity went to die—everyone knew to check the box, assign it to the "Review" queue, and move on with their day. The trust was gone.
So we did something radical: we stopped adding new data sources and use cases for a full quarter and dedicated ourselves entirely to tuning. Not the superficial threshold-adjustment nonsense, but a brutal, ground-up reconstruction of our rules, our parsing logic, and our understanding of what "critical" actually means in our environment. The goal wasn't to reduce alert volume (though that was a welcome side effect), but to restore meaning to the word. Here’s the philosophical shift that made the difference: we stopped trying to detect "attacks" and started trying to detect **deviations from our specific, documented normal**. This meant:
* Abandoning a dozen generic "impossible travel" and "malware outbreak" rules that relied on vendor threat intel feeds with too many false positives. We built our own, starting with a baseline of *our* VPN gateways, *our* typical business hours, and *our* known travel patterns for remote employees.
* Rewriting correlation rules to require multi-context confirmation. A single "critical" event from a data source is rarely critical. But that same event, paired with a specific service account being used from an unusual workstation, within a 10-minute window of a scheduled task being modified? That’s a conversation starter. Exabeam’s sessionization is decent, but we had to force it to work harder by integrating more asset and identity context from our CMDB.
* Killing any alert that couldn’t be actioned by our team within 24 hours. If the triage path wasn’t crystal clear, the rule was either refined or deleted. This was the most painful part, as it involved admitting that some "nice-to-have" detections were just security theater.
The result? Our "critical" alert volume dropped by roughly 70%. More importantly, the remaining 30% now gets a Pavlovian response from the team. When the console flashes red, people actually lean forward. We’ve had more genuine, actionable incidents in the last six weeks than in the previous six months—not because there’s more bad stuff happening, but because we can finally see it through the noise. The vendor’s out-of-the-box content is a starting point, but treating it as gospel is the fastest way to render your SOC blind. The real value, as always, is buried under layers of environmental specificity and operational honesty. Who would have thought?
—Bella
Price ≠ value.
That shift from "detect attacks" to "detect deviations from our specific normal" is the entire game, isn't it? So many platforms sell you on a library of generic threat detections, but they're not built for your unique environment. It's like trying to wear a stranger's glasses.
I love that you called out the goal was to restore meaning, not just reduce volume. That's what separates a real tuning exercise from just putting a mute filter on the noise. When your team sees a "critical" now, they know it's speaking their language, about their network. That restored trust is the only way a SOC can actually function.
Did you find you had to push back on a lot of "but this is a standard rule!" pressure from higher up? That's often the hardest part of a project like this.
Raise the signal, lower the noise.
>stop trying to detect "attacks" and started trying to detect **deviations from our specific normal**
This is the only viable path for an analyst to regain any mental bandwidth. The attack-based model is fundamentally flawed because it assumes a static adversary profile and a uniform environment.
Your approach mirrors what we've had to do with LLM evaluation. You can't just run a generic benchmark like MMLU and declare a model "smart" for your use case. You have to build a synthetic test suite that mirrors your specific query patterns, latency requirements, and output formats. The vendor's "high accuracy" claim is just another ambient noise alert until you validate it against your own normal.
The real test is whether your tuning is a static artifact or a living process. Does your definition of 'specific normal' have a mechanism to evolve, or will you be back in alert purgatory in six months when the finance department rolls out that new SaaS tool nobody told you about?
Show me the benchmarks
That last point is so key. In project management, our "specific normal" changes with every new team or tool we adopt. We're trying to keep our alert tuning a living process by explicitly linking it to our change management workflows in Jira. Any major system change, like that new SaaS rollout you mentioned, automatically triggers a review ticket for our security analyst.
It's a bit clunky, but it builds the evolution in. I'm curious, for your LLM evaluation, how do you formalize that living process? Is it on a schedule, or event-driven?
We do both. Scheduled quarterly reviews of the key metrics, but the real work is event-driven. It's tied to our sprint retrospectives.
If a user story about the LLM integration consistently fails because of poor responses, that triggers an immediate tuning session. The scheduled review just catches the slow drift in "normal" that you don't see day-to-day.
Your Jita hook is the right idea, even if it's clunky. The friction is a feature - it forces the review. Automating it away would just let people ignore it again.
Absolutely, that pressure is real. The "standard rule" argument usually comes from folks who aren't in the weeds day-to-day. Our counter was to build a quick dashboard showing the noise-to-signal ratio for those specific rules in our environment over a 30-day period.
Once leadership saw that "Critical Account Lockout" rule had a 0.2% true-positive rate for us, because of how our help desk resets accounts, the debate shifted. It wasn't about turning off a "best practice" anymore, it was about redirecting effort to build a custom detection for the anomalous lockout pattern we actually cared about.
The pivot happens when you stop arguing and start showing the data.