Anomaly Detection Alerts

Anomaly detection alerts utilize artificial intelligence algorithms to analyze incoming logs and predict their expected behavior for 24 hours. When a log falls above or below a predefined threshold, something unusual may have occurred, indicating an opportunity for corrective action.

For example, an anomaly detection alert can help you discover when a transaction’s response time exceeds its usual duration, allowing you to pinpoint and address performance bottlenecks. Or it can alert you when the outgoing traffic of a host exceeds its usual levels, indicating a potential security breach.

Dynamic alerts are powered by our Streama© technology, which allows them to run on the Coralogix monitoring pipeline at a third of the cost, without prior indexing.

Create an alert

Set up a logs-based anomaly detection alert to notify you if a log exceeds an AI-generated baseline threshold.

1

Navigate to Alerts > Alert Management. Click New Alert.

2

When defining your alert conditions, select to be alerted when an event is more-than-usual compared to the baseline condition.

3

Define the alert conditions.

4

Add one or more group-by keys. An alert is triggered whenever the condition threshold is met for a specific aggregated key within the specified time window. Our machine-learning model establishes the baseline standard for every group-by key.

5

[Optional] Configure the advanced settings, including custom evaluation delay and percentage deviation.

6

Finalize the alert setup.

Limitations

Our machine-learning model establishes the baseline standard for your logs for every group-by key in your alert definition. It is applied daily for the next 24 hours, using data from the past 7 days, and is based on a maximum of 500 permutations.

Support

Need help?

Our world-class customer success team is available 24/7 to walk you through your setup and answer any questions that may come up.

Contact us via our in-app chat or by emailing [email protected].

Define Alert Details

Next Immediate Notifications