Explanation:
First table: BehaviorAnalytics
Joined table: AuditLogs
To detect when a user creates an unusually large number of Azure AD user accounts in Microsoft Sentine l, you should leverage UEBA signals from the BehaviorAnalytics table and enrich them with Azure AD audit data from AuditLogs . The BehaviorAnalytics table contains UEBA-derived insights (for example, the ActivityInsights flag and UsersInsights ) and normaliz ed activity fields such as ActionType (e.g., " Add user
" ). Filtering BehaviorAnalytics for ActionType == " Add user " and ActivityInsights has " True " targets activities that the UEBA engine already assessed as anomalous, reducing noise and focusing on outlier s.
Then, join these anomalies to the AuditLogs table to pull authoritative Azure AD audit details (target object, initiator, correlation, and operation context). This combination aligns with Sentinel guidance: use BehaviorAnalytics for anomaly detection an d AuditLogs for the Azure AD operational record. Sorting by TimeGenerated and projecting user and insight fields completes the hunting query so analysts can review who triggered unusual "Add user" bursts and with what context.
Therefore, complete the query by selecting BehaviorAnalytics as the primary dataset and AuditLogs in the join(...) .