Description
'Anomaly Rules generate events in the Anomalies table. This scheduled rule tries to detect Anomalies that are not usual, they could be a type of Anomaly that has recently been activated, or an infrequent type. The detected Anomaly should be reviewed, if it is relevant enough, eventually a separate scheduled Analytics Rule could be created specifically for that Anomaly Type, so an alert and/or incident is generated everytime that type of Anomaly happens.'
- Rule type
- Scheduled
- Version
- 1.0.3
- Query frequency
- 1h
- Query period
- 4d
- Trigger
- gt 0
KQL query
Original query, unchanged.
// You can leave out Anomalies that are already monitored through other Analytics Rules
//let _MonitoredRules = dynamic(["TestAlertName"]);
let query_frequency = 1h;
let query_lookback = 3d;
Anomalies
| where TimeGenerated > ago(query_frequency)
//| where not(RuleName has_any (_MonitoredRules))
| join kind = leftanti (
Anomalies
| where TimeGenerated between (ago(query_frequency + query_lookback)..ago(query_frequency))
| distinct RuleName
) on RuleName
| extend Name = tostring(split(UserPrincipalName, "@")[0]), UPNSuffix = tostring(split(UserPrincipalName, "@")[1])
Declared entities
Source provenance
GitHubDisplayed values come from files in Azure/Azure-Sentinel. They describe the published template, not your workspace configuration.
- Commit
629d1d3↗- Source identifier
d0255b5f-2a3c-4112-8744-e6757af3283a
Additional source files 1
Detections/Anomalies/UnusualAnomaly.yamlsource ↗GSTEP / CATALOG TRACKING
Added to catalog : 16 Sept 2026 · 05:49 UTC
Last change observed : 16 Sept 2026 · 05:49 UTC