Understanding Anomaly Detection: When Visibility Became Insight

By 2019, organizations were starting to see what was really on their networks. Visibility had improved. Discovery was improving. Device classification was becoming more reliable.

But something else became obvious: knowing what was connected didn’t mean knowing what was happening.

Because compromise didn’t always look like malware. And risk didn’t always look like a spike.

Sometimes, the earliest signs of an attack looked like normal activity.

The Problem: Security Teams Were Drowning in Noise

Security systems were built to catch known threats. They looked for indicators. They relied on signatures. They treated every environment the same.

But modern networks weren’t uniform. And connected devices behaved differently than traditional endpoints.

Unmanaged devices didn’t generate endpoint logs. OT systems didn’t behave like laptops. Medical devices often ran legacy operating systems and couldn’t be updated.

That meant traditional detection systems missed the earliest signals.

Not because teams weren’t looking.
Because they weren’t looking in the right way.

The Shift: Behavioral Analytics

To catch modern threats, teams needed to stop relying only on known patterns and start understanding normal behavior.

This is where behavioral analytics and anomaly detection became essential.

Instead of searching for signatures, security teams began building baselines and watching for deviation.

They could ask:

• What does normal communication look like for this device?
• Who does it typically talk to?
• What protocols does it use?
• How often does it connect?
• What does it never do?

Then, when something changed, it mattered.

When Anomaly Detection Became Real

The early breakthrough was realizing that devices have habits.

A monitor in an ICU doesn’t suddenly start scanning the network. A building management controller doesn’t begin communicating with a domain it’s never contacted before. A badge reader doesn’t begin sending data to an unknown external server.

When these changes happen, they aren’t random.

They’re meaningful.

Anomaly detection transformed visibility into insight by revealing when devices behaved unexpectedly.

This didn’t eliminate noise.
It changed the signal.

From Alerts to Understanding

Behavioral analytics helped teams connect the dots.

Instead of showing thousands of alerts, systems could surface a small number of meaningful deviations.

Instead of reacting after compromise, teams could detect unusual behavior early, before it became catastrophic.

This was the beginning of a new kind of intelligence.

Not just what was connected, but what was happening.