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Description

The following analytic triggers on a high risk sign-in against Azure Active Directory identified by Azure Identity Protection. Identity Protection monitors sign-in events using heuristics and machine learning to identify potentially malicious events and categorizes them in three categories high, medium and low.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Risk
  • Last Updated: 2023-12-20
  • Author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
  • ID: 1ecff169-26d7-4161-9a7b-2ac4c8e61bea

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1586 Compromise Accounts Resource Development
T1586.003 Cloud Accounts Resource Development
T1110 Brute Force Credential Access
T1110.003 Password Spraying Credential Access
Kill Chain Phase
  • Weaponization
  • Exploitation
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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 `azure_monitor_aad` category=UserRiskEvents properties.riskLevel=high 
| rename properties.* as * 
| stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by src_ip, activity, riskLevel, riskEventType, additionalInfo 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `azure_active_directory_high_risk_sign_in_filter`

Macros

The SPL above uses the following Macros:

:information_source: azure_active_directory_high_risk_sign-in_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • category
  • properties.riskLevel
  • user
  • src_ip
  • properties.activity
  • properties.riskEventType
  • properties.additionalInfo

How To Implement

You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the RiskyUsers and UserRiskEvents log category in the azure:monitor:aad sourcetype.

Known False Positives

Details for the risk calculation algorithm used by Identity Protection are unknown and may be prone to false positives.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
54.0 60 90 A high risk event was identified by Identify Protection for user $user$

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

source | version: 2