Beyond Alert Fatigue: How a Data-First Approach Is Reshaping Security Operations

If you've spent any time in a Security Operations Center, you know the drill. Screens filled with alerts. Analysts context-switching between a dozen different dashboards. The constant, nagging question: is this one actually important?

Human Managed's founder Saleem has spent 25 years in the cybersecurity field, and his assessment is blunt: the fundamental challenges—alert volume, analyst burnout, lack of context—remain largely unsolved despite waves of new technology.

The Tool Trap

Most organizations approach security operations as a tool problem. When something breaks, buy new software. When alerts pile up, add another dashboard. When analysts burn out, hire more people.

The Human Managed team argues for treating security as a data problem instead. Rather than building yet another detection tool, we focused on how data flows through security operations—and where that flow breaks down.

CEO Karen Kim draws a distinction between traditional approaches that present data to humans versus making data the central player. The difference: front-loading analysis and contextual understanding so that by the time information reaches an analyst, the heavy lifting is already done.

The Gap That Actually Matters

There's a metric that rarely appears on security dashboards but might be the most important one: the gap between when something happens and when someone makes a decision about it.

Consider a practical example: file transfer monitoring. Uploading files to cloud storage is normal activity. But telemetry can reveal patterns—a user uploading to a non-organization-owned online drive. That's one data point.

Add context through automated enrichment: the user has elevated permissions, access to confidential data, and resigned a week ago.

The individual facts weren't alarming. The pattern—assembled automatically, enriched with context—absolutely is. And critically, this analysis happened in microseconds rather than hours of manual investigation.

Building Trust Through Explanation

Any system that automates triage decisions must establish credibility with analysts. The Human Managed approach uses AI not just for detection but to articulate why a decision was made. Statistical models handle pattern recognition and scoring. Language models communicate findings in human-readable form.

The messaging design also combats alert fatigue deliberately. New customers receive a baseline configuration filtering for only critical and high-severity alerts—because user research revealed that people who want everything become overwhelmed within days and stop checking entirely.

What Actually Changes

The platform demonstrates four-minute average triage times—compared to industry standards that often stretch to hours. Alerts arrive enriched with asset context, threat intelligence, and business impact assessments. AI provides not just severity scores but explanations and recommended actions.

The cybersecurity industry has spent decades building sophisticated detection capabilities while largely ignoring what happens after detection. Alert fatigue isn't a bug—it's the predictable outcome of prioritizing identification over action.

Twenty-five years is long enough to solve the same problems. The technology finally exists to do something different.

Want to see how Human Managed approaches SecOps differently? Check out the full video series here on the Human Managed YouTube Channel where team members walk through the platform, philosophy, and technical architecture behind modern security operations.

This article is written based all videos in our Secops video series.