Published Friday, July 31, 2026 at 12:06 PM PT
Burbank · Friday, July 31, 2026 · 12:06 PM · 94°F, 37% humidity, wind 2 mph E, 29.39 inHg, UV 0, PM2.5 6
Your alert feed grew 29% this week and immediately I can hear the chorus: “The whole house is on fire!” Spoiler alert—it’s not. We’re watching a system that’s operating roughly as intended, which is to say chaotically, loud, and somehow still functional. Let me walk you through the actual shape of this noise so you can tell the difference between a real problem and just background radiation.
Start with the good news wrapped in mediocre packaging: incident velocity is fine. Three hundred eighty-one alerts escalated to incidents, three hundred seventy-eight resolved, typical time-to-resolution around three hours. That’s not a flex, but it’s not a code-red collapse either. You’re essentially treading water and occasionally surfacing. The system catches problems, people fix them, life goes on. The queue isn’t growing faster than it drains, which means we’re not bleeding out yet.
What those numbers actually represent is worth understanding. Three hours to resolution across three hundred seventy-eight incidents means your on-call response is working—someone’s getting paged, context is loading, and systems are getting fixed before cascading failures multiply. That’s the real metric underneath “time-to-resolution.” It’s not the time between alert and fix; it’s the time between alert and the system returning to nominal state. For a network with the complexity of yours—multiple service tiers, distributed components, networked environmental sensors, security detection running in parallel with legitimate infrastructure—three hours is legitimately acceptable. Not aspirational. Acceptable. The incidents that resolve in thirty minutes are the ones someone’s already seen before. The ones that take six hours are usually the ones where the root cause isn’t obvious from the alert alone and requires investigation across multiple systems.
The escalation rate tells its own story. Three hundred eighty-one alerts became incidents; three hundred seventy-eight got resolved. That means your alert threshold tuning is approximately at the border between “catches real problems” and “too aggressive.” A perfect system would have zero escalations (no problems), which is impossible. A dying system would have escalation rate approaching one hundred percent (everything becomes critical). At roughly 99% escalation-to-resolution, what you’re seeing is an alerting system that’s mostly working—it’s loud, but it’s not creating monsters. Three alerts that didn’t escalate to incidents were either false positives that got manually dismissed, or they resolved themselves before human intervention was needed.
Ferengi Rule of Acquisition #143 says the family of fools is ancient—and there’s something eternal about an infrastructure team that inherits a system built by previous versions of itself, each generation adding their own layer of specialized screaming. Your network is a perfect example. Someone set up a service. Someone else added monitoring. A third person added a backup system. A fourth person added environmental sensors so they could tell when the utility room was flooding without walking downstairs. A fifth person added network-wide security monitoring. Now you’ve got a system where every one of those subsystems is firing alerts, some of them false, some of them interdependent, and some of them genuinely useful.
Now let’s talk about what’s actually happening. The chronic alert categories reveal a system that’s firing in very specific ways, and the trend direction matters more than raw count. Your backup subsystem is easing week-over-week across two distinct alert types, which means either the thing finally stopped spasming or we tuned the thresholds better. Either way, fewer false alarms is fewer false alarms. The backup system lives in the infrastructure layer that’s simultaneously critical (you need it for disaster recovery) and annoying (it generates noise about retention policies, snapshot timing, and incremental transfer sizes that almost never matter operationally). When backup alerts decline, it usually means one of three things: the backup job stopped running at weird times, the thresholds for “something unexpected happened” got smarter, or you finally fixed whatever misconfiguration was causing it to complain daily.
Trafficcam alerts are declining too—those things have a talent for generating noise about absolutely nothing. Someone set up network cameras to monitor the infrastructure room, the power distribution area, maybe the network closet. Those cameras live on the network, they have IP addresses, they send heartbeat packets. When a camera goes offline for a network blip, an alert fires. When a camera comes back online, an alert fires. When someone checks the camera and its lens is dirty, you get three different alerts from the security monitoring system trying to decide if this is a breach or just a need for a cleaning cloth. Good riddance to that volume. The fact that trafficcam alerts are down week-over-week either means someone fixed the actual device stability (less likely), or the monitoring rules got less aggressive about what counts as a problem (more likely).
But we’ve got rising trouble in three categories, and here’s where it gets interesting. Your scheduler is firing more, and it’s firing consistently enough to suggest this isn’t transient. Scheduler alerts are where the rubber meets the road in automation infrastructure. The scheduler is the daemon that runs your periodic jobs—backups at midnight, health checks every five minutes, data uploads every hour, log rotation, cache cleanup, all of it. When scheduler alerts rise, it means one of several things is happening, and the implications change depending on which one.
First possibility: you’ve added load. If you added a new periodic job, or if an existing job started running more frequently, the scheduler has more work to do and is more likely to miss deadlines or report that something ran late. This is actually information—it tells you that your automation infrastructure is hitting its capacity ceiling. Schedulers aren’t magical; they’re just processes managing a list of tasks. When the list gets long enough or the tasks get resource-intensive enough, the scheduler itself becomes a bottleneck. You’ll start seeing “job X missed its window,” “job Y didn’t complete before job Z needed to start,” that kind of timing drift. It’s not flashy, but it’s important. A scheduler that can’t keep up with its own task list will eventually cause cascade failures: missed backups create gaps in recovery windows, missed health checks mean problems don’t get detected, missed cleanups mean temporary data accumulates.
Second possibility: the scheduler is starting to struggle under the existing load. This is subtly different from adding load. It means the system was fine before, but something changed about resource availability. Maybe another process is now consuming CPU or memory that the scheduler needs. Maybe you upgraded the database and it’s now using more I/O capacity. Maybe network latency increased and all the scheduler jobs that involve remote operations are now taking longer to complete. The alerts might look the same (job ran late, job failed, job timed out), but the root cause is resource contention, not too many tasks.
Third possibility: someone configured an alert threshold so tight it catches every hiccup as catastrophic. This is remarkably common in monitoring systems. Someone sets a threshold at “this job should never take more than 60 seconds,” and then doesn’t account for the fact that on Tuesdays when backups are running, that job might legitimately take 90 seconds because the disk is busy. Now every Tuesday you get an alert about a threshold violation on something that’s actually fine. This is the alert tuning problem in its purest form: the alert is technically accurate (the job did take 90 seconds), but operationally useless (90 seconds on Tuesday is normal).
Scheduler alerts aren’t usually noise—they’re your system admitting it can’t keep up. Worth a look, but not a panic. You need to know: are we adding jobs, are we starving the scheduler of resources, or are we yelling about normal variance? The answer changes your next move.
Security alerts rising? That’s actually your detection working, not necessarily your environment falling apart. This is where understanding your security architecture matters. The red-team and blue-team are both running, meaning you’ve got machines actively trying to break in (red-team, the attack simulator) and machines actively watching for intrusions (blue-team, the SIEM). When both are running in the same house, blue-team inevitably catches more stuff—some of it real, some of it the red-team doing its job. The boundary between “legitimate activity” and “attack attempt” is blurry when the attacker is you and the defender is also you.
This is why you have the purple-team. The purple-team is the referee. It runs after the red-team creates test attacks and the blue-team detects them, and it asks the critical question: did blue-team catch what was supposed to be caught? This is detection validation. It’s not about whether the alerts fired—they fired, clearly, you’re looking at rising security alerts. It’s about whether the alerts were meaningful. If purple-team says “yes, all those alerts were legitimate detections of actual red-team activity,” then your security posture is excellent and you’re just running a lot of drills. If purple-team says “65% of those alerts were false positives and the red-team managed to do something that didn’t trigger any detection,” then you’ve got actual problems: either your detection rules are too broad (catching normal activity as attacks) or too narrow (missing real attacks), or both.
The detection-validation scorecard is the raw data here. Pull that scorecard and read it carefully. If it came back clean and shows high detection accuracy with low false positives, you’re not being attacked—you’re just running the weapons-grade equivalent of a fire drill. Keep doing that. If it shows that blue-team missed some attack vectors or generated tons of false positives, then the rising security alerts are actually pointing at a tuning problem in your detection rules, not an increase in actual attack activity.
Unifi and soil sensors both rising—and here’s the comedy in infrastructure work—these are often the loudest devices in the quietest moments. Unifi is your network switching gear. Those devices manage the actual network traffic flowing through your infrastructure. Rising Unifi alerts usually mean someone changed a configuration, or a device is drifting into a marginally unstable state. Link flapping is the classic one: a network port keeps going up and down, up and down, usually because of a cable issue or a port that’s starting to fail. Or maybe aging hardware is starting to wear out. Or a switch that’s seen better days. Maybe you added a new device and it’s causing STP recalculations, creating temporary loop-detection events. Maybe someone configured a VLAN trunk wrong and now spanning tree is constantly recalculating its topology. Any of these things will cause rising Unifi alerts.
Soil sensors are environmental IoT. They measure humidity, temperature, occasionally soil moisture if you’ve got them in grow beds or garden setups. They’re monitoring for things like water intrusion (burst pipe, flooding), temperature excursions (HVAC failure, hot day), or actual anomalies that might need physical intervention. They also go down. They reconnect weirdly. Battery-powered sensors run out of batteries. WiFi sensors get signal loss and reconnect later. They fire alerts about moisture levels like they’re calling 911 over a light drizzle. Both categories are verbose by nature; both escalate to incident too easily. This is the sound of systems that haven’t learned to distinguish between “needs immediate attention” and “thought about complaining today.”
The 29% headline is doing work on your anxiety that the actual data doesn’t justify. You’ve got three categories easing, three categories rising, and a system that’s resolving incidents faster than it creates them. In a one-hundred-device network with thirty-three smart lights and an unreasonable amount of services—which, let’s be clear, is a setup designed by someone who reads “distributed systems” and thinks “sounds fun, let’s add more”—this is acceptable chaos. Not good. Acceptable. Good would be fewer alerts and faster resolution. Acceptable is the state you’re in: you’re catching problems, fixing them, and your system reliability isn’t degrading.
Nine new devices joined the network this week with zero rogue access points flagged. That’s either people bringing things in intentionally—a guest device, new hardware, someone’s personal gadget—or your DHCP server handing out addresses without getting audited carefully, and either way, the security posture caught zero violations. Network stability is holding. No unexpected devices, no suspicious activity on the new devices, no lateral movement attempts. New devices without security incidents is the baseline for healthy network operations.
Here’s what to do: audit the scheduler alert threshold first. It’s probably too aggressive. Pull the scheduler logs for the past week and look at the job execution times. Find the p99 execution time (the time that 99% of jobs finish by) and set your alert threshold at 1.5x that. This absorbs normal variance without yelling about every hiccup. Document what the purple-team scorecard said about those security detections—confirm they’re validation noise, not a real trend. If the scorecard says “all green,” leave the security rules alone. If it says “caught 65% with 20% false positive rate,” then you’ve got detection work to do, but that’s a separate conversation from this alert surge. Schedule someone to walk the Unifi infrastructure and soil sensors to see if there’s actual hardware aging or just configuration drift. That walkthrough takes an hour and tells you whether you’re dealing with hardware about to fail or monitoring thresholds that need adjustment.
The backup volume decline is a win—leave it alone. The trafficcam volume decline is a win—leave it alone. Two out of three rising trends are probably tuning problems, not architectural problems. Fix the tuning, watch it for a week, and come back when the pattern changes.
Until then, the house is not on fire. It’s just louder than usual.