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Phish and Malware received by user vs total amount of email

kql MEDIUM Azure-Sentinel
EmailEvents
huntingmicrosoftofficial
This rule was pulled from an open-source repository and enriched with AI. Validate in a test environment before deploying to production.
View original rule at Azure-Sentinel →
Retrieved: 2026-05-22T23:00:00Z · Confidence: medium

Hunt Hypothesis

Users receiving a high volume of phishing and malware emails compared to legitimate emails may indicate targeted compromise, as adversaries often use email as an initial entry vector, and proactive hunting can help identify and mitigate potential breaches before they escalate. SOC teams should actively hunt for this behavior in Azure Sentinel to detect early signs of credential theft, malware distribution, or lateral movement attempts.

KQL Query

let UserToAnalyze="[email protected]";
EmailEvents
| where RecipientEmailAddress==UserToAnalyze
| project RecipientEmailAddress, ThreatTypes
| evaluate pivot(ThreatTypes)
| sort by RecipientEmailAddress asc

Analytic Rule Definition

id: 229ec9f6-05cc-483d-b3dc-35f47575a5aa
name: Phish and Malware received by user vs total amount of email
description: |
  How much phish and malware emails vs good emails received the user in the given timeframe.
requiredDataConnectors:
- connectorId: MicrosoftThreatProtection
  dataTypes:
  - EmailEvents
query: |
  let UserToAnalyze="[email protected]";
  EmailEvents
  | where RecipientEmailAddress==UserToAnalyze
  | project RecipientEmailAddress, ThreatTypes
  | evaluate pivot(ThreatTypes)
  | sort by RecipientEmailAddress asc

Required Data Sources

Sentinel TableNotes
EmailEventsEnsure this data connector is enabled

References

False Positive Guidance

Original source: https://github.com/Azure/Azure-Sentinel/blob/main/Hunting Queries/Microsoft 365 Defender/General queries/Phish and Malware received by user vs total amount of email.yaml