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An alert touches multiple segments of the network, representing the challenge to reduce false positives
Deep Network Observability

How to Reduce False Positives with Shared Context

False positives are rarely caused by too many alerts, but by missing context. Let’s say a spike in outbound traffic shows up in the firewall logs. Around the same time, ...

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Magnifying glass on top of a diagram that looks like both a fingerprint and a network, representing end-to-end network troubleshooting
Network Operations

Why Most Performance Investigations Start in the Wrong Place

A padlock silhouetted against a dark background, surrounded by a spray of white particles, representing password spraying detection.
Security Operations

What Password Spraying Looks Like in Raw Network Telemetry

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Blocks of data in a digital environment. Most of the blocks are clear but one is red, representing AI-powered anomaly detection
Network Security

AI-Powered Anomaly Detection: Benefits, Techniques, and Challenges

As networks scale and data volumes grow, detecting what doesn’t belong becomes increasingly difficult. Modern environments generate vast datasets

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Waves of digital information form the shape of a brain, representing AI for network security and monitoring
Deep Network Observability

The Benefits of AI for Network Security and Monitoring

We’ve seen more and more organizations leveraging AI for network security and monitoring. The reason? Modern networks are intricate

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what is machine learning
Security Operations

What is Machine Learning: The Hype, the Promise, and the Reality

Although the concepts of artificial intelligence and machine learning are not new, they have recently garnered mainstream attention. As

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