detection-rules
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Unusual City for an Azure Activity Logs Event

A machine learning job detected Azure Activity Logs activity that, while not inherently suspicious or abnormal, is sourcing from a geolocation (city) that is unusual for the event action. This can be the result of compromised credentials or keys being used by a threat actor in a different geography than the authorized user(s).

Rule type: machine_learning
Rule indices:

Rule Severity: low
Risk Score: 21
Runs every: 15m
Searches indices from: now-2h
Maximum alerts per execution: ?
References:

Tags:

  • Domain: Cloud
  • Data Source: Azure
  • Data Source: Azure Activity Logs
  • Rule Type: ML
  • Rule Type: Machine Learning

Version: ?
Rule authors:

  • Elastic

Rule license: Elastic License v2

This rule requires the installation of associated Machine Learning jobs, as well as data coming in from Azure Activity Logs.

Once the rule is enabled, the associated Machine Learning job will start automatically. You can view the Machine Learning job linked under the "Definition" panel of the detection rule. If the job does not start due to an error, the issue must be resolved for the job to commence successfully. For more details on setting up anomaly detection jobs, refer to the helper guide.

The Azure Activity Logs integration allows you to collect logs and metrics from Azure with Elastic Agent.

  • Go to the Kibana home page and click “Add integrations”.
  • In the query bar, search for “Azure Activity Logs” and select the integration to see more details about it.
  • Click “Add Azure Activity Logs”.
  • Configure the integration.
  • Click “Save and Continue”.
  • For more details on the integration refer to the helper guide.

Framework: MITRE ATT&CK