Why Traditional SIEMs Generate Too Much Noise

TL;DR

Traditional SIEMs were designed for a “static” world — and have suffered what we call a “throughput failure”: They produce so many alerts that analysts are overwhelmed, and can’t keep up. An estimated 83% of those alerts turn out to be false positives, so security teams spend most of their time (and most of their money) on manual triage instead of actual defense. At Secure.com, we're changing that. Our Digital Security Teammates automate the mechanical work that bogs down security teams so they can focus on the stuff that really matters — high-impact decisions and strategies.

Key Takeaways

• Context is King: Traditional SIEMs lack environmental awareness, treating every anomaly as an isolated threat regardless of asset value or business impact.
• The 80/20 Rule of Noise: Research shows approximately 45-83% of SIEM alerts are false positives, with Ponemon Institute reporting roughly 45% and industry studies citing up to 83%, consuming nearly three hours of an analyst's day in manual triage.
• Operational Debt: Tool sprawl and fragmented dashboards force analysts into "unpaid integration labor," stitching together logs that should be connected automatically.
• The Leverage Shift: Moving to a "New SOC" model uses AI to handle 70% of manual triage, reducing noise and speeding up response times by 45–55%.

Introduction

Research from Orca Security shows 59% of teams receive more than 500 cloud security alerts daily. That's one every two minutes during an 8-hour shift.

55% of cloud security professionals admit they miss critical alerts—not because they're careless, but because they're drowning. According to research from Orca Security, 59% of teams receive more than 500 cloud security alerts daily, and the sheer volume makes it nearly impossible to separate real threats from background noise.

Your SIEM isn't broken—it's just designed for a world that no longer exists.

The Static Rule Trap: Why Legacy Logic Fails Modern Clouds

Conventional SIEMs use “if-then” rules that assume a consistent network environment — like those found in traditional data centers. When these systems are deployed in the cloud, where things can scale up and down rapidly and even individual servers can appear and disappear in the blink of an eye, they don’t work very well.

Here's the problem: clouds don't sit still.

• Your security information and event management (SIEM) tool raises an alarm every time Netflix creates 500 new EC2 instances to cope with a surge in streaming traffic.
• Or when Kubernetes increases the number of pods for a microservice from three to 30 in less than two minutes.

This is because they are designed to work in a world where IT infrastructure changes at a glacial pace— and by “glacial,” we mean “measured in months or years.” Back then, it was common for servers to be named something like PROD-SERVER-01 and stay up and running for months at a time.

But we’re now well into the era of cloud computing—where workloads can appear and disappear in seconds (sometimes even less), containers can exist for only a few seconds before being terminated, and IP addresses can change multiple times per hour.

In other words, your SIEM was made for a bygone age: one in which your average data center didn’t see much alteration on a daily basis. Now everything changes constantly, at breakneck speed.

And because auto-scaling groups behave exactly as their name implies (i.e., they automatically scale), analysts get up in the morning only to find they’ve wasted time chasing down alerts about activity that took place at 3 am— precisely when these groups were supposed to be doing what they do!

The "Swivel-Chair" Effect: Fragmentation as a Noise Multiplier

Most organizations manage between 60 and 75 disconnected security tools. Because these tools exist in silos, the SIEM cannot correlate signals across endpoint, identity, and cloud layers. Analysts are forced to manually export CSVs and switch dashboards just to understand if an alert is real, creating a "fragmentation tax" on every investigation.

According to Sumo Logic's research, 55% of security teams struggle with too many point solutions, and 40% report juggling excessive siloed tools.

Picture this: Your SIEM throws an alert. Suspicious login from an unfamiliar location.

To investigate, you need to:

1. First, check the identity provider to see if the user's login patterns are normal.
2. Next, go to your EDR console to check whether the endpoint involved is infected with any malware.
3. Then, access your cloud service and find out if the user usually logs into this resource from there.
4. After that, open your SOAR platform and verify whether there is an existing ticket for the incident.
5. Export logs from three different systems into spreadsheets.
6. Manually match time stamps across various time zones.

By the time you piece it together—30 to 60 minutes later—you discover the user is traveling for work and logged in from their hotel. False positive.

Research from the Ponemon Institute shows roughly 45% of SIEM alerts are false positives. Separately, MSPs using seven or more tools experience 64% more alert fatigue than those using four or fewer—a clear indicator of the 'tool sprawl' problem plaguing modern SOCs.

This isn't analysis. It's data archeology. And you're doing it 400 times per day because none of your tools talk to each other.

Measuring the Wrong Things: When "Security Theater" Outshines Outcomes

Legacy SOC metrics often prioritize ticket volume and "alerts closed" over actual risk reduction. This rewards the generation of more noise rather than high-fidelity signals. The result is a system that looks busy on a dashboard but leaves a significant portion of daily alerts uninvestigated due to sheer volume, with some organizations reporting 50-70% of alerts receive no investigation.

Check any traditional SOC dashboard and you'll see metrics like:

• Alerts processed: 11,247
• Tickets closed: 8,932
• Average time to close: 4.2 hours

Looks productive, right? Wrong. These numbers measure activity, not security.

If 83% of those alerts are false positives, your team just burned 7,400+ hours investigating nothing. That's like running on a treadmill and calling it progress because you took 10,000 steps.

The incentive structure is backwards. Analysts get rewarded for closing tickets fast, not for finding real threats. So when an alert comes in at 4:45 PM on Friday, the safest move is to mark it "resolved - false positive" and go home.

In 2020, Forrester reported that SOC teams received 11,000 alerts daily, and 55% of cloud security professionals admitted to missing critical alerts.

This creates a perverse cycle: More alerts make teams look busier. Busier teams ask for more budget. More budget funds more tools. More tools generate more alerts. Nobody stops to ask if any of this actually stopped an attack.

Meanwhile, the APT group that's been quietly exfiltrating your customer database for the past six weeks? Still undetected. Because your team was too busy investigating autoscaling events.

From Reactive Triage to Autonomous Investigation

The solution isn't a better dashboard—it's a change in work distribution. By deploying Digital Security Teammates, the routine grind of gathering context and evidence is automated. This allows analysts to start their day with a complete "case" rather than a raw alert, reducing manual security work by 70%.

Most "AI SOC" products still make you do the work. They just give you a chatbot to ask questions while you're doing it. That's not leverage—that's just a more sophisticated interface for the same manual work.

Real automation means your team starts the day with investigated cases, not raw alerts. When a login anomaly fires at 2 AM, a Digital Security Teammate should already:

• Check if the user is traveling (calendar integration)
• Verify the device fingerprint matches their known devices
• Cross-reference the IP against threat intelligence feeds
• Review their access patterns from the past 30 days
• Assess whether the resource they accessed is critical
• Determine if similar behavior triggered alerts for other users

By 8 AM, your analyst doesn't see "Suspicious Login - Unknown Location."

They see:
"Low-risk login event - User confirmed traveling for work based on calendar. Device matches registered laptop. No privilege escalation attempted. Automatically marked as benign. Review if pattern changes."

Digital Security Teammates reduce manual triage workload by 70% and improve Mean Time to Respond (MTTR) by 45–55%, but the real benefit isn't speed—it's giving your team time to think.

Instead of spending three hours each morning triaging false positives, your analysts can:

• Hunt for advanced persistent threats
• Review security architecture gaps
• Build detection rules for emerging attack patterns
• Actually talk to the business about risk

That's what "AI-first security" actually means. Not replacing humans. Giving them back their brains.

FAQs

Conclusion

Cybersecurity is not broken, it’s just not able to keep up with the job at hand! The traditional SIEMs have become “noise factories” because they depend on humans to handle things that happen at machine speed.

By adopting AI-first operations, teams will be able to stop “playing whack-a-mole” with alerts and instead build resilient and proactive security programs. The reason why this works so well is actually quite simple– attacks can scale infinitely but humans can’t!

If we want our security operations centers (SOCs) to succeed in 2026 and beyond, we need to stop thinking more analysts is the answer. Instead, focus on giving each analyst 10x the leverage through automation that actually works.

This means changing how we measure success too. Don’t measure how many alerts you close; measure how many real threats you catch before they become breaches.

Ready to cut SIEM noise by 70% and let your team focus on real threats? Book a demo and see how Secure.com's Digital Security Teammates transform alert chaos into actionable intelligence.