Active Directory Lateral Movement Identified

name: Active Directory Lateral Movement Identified
id: 6aa6f9dd-adfe-45a8-8f74-c4c7a0d7d037
version: 10
creation_date: '2023-04-19'
modification_date: '2026-05-13'
author: Michael Haag, Splunk
status: production
type: Correlation
description: The following analytic identifies potential lateral movement activities within an organization's Active Directory (AD) environment. It detects this activity by correlating multiple analytics from the Active Directory Lateral Movement analytic story within a specified time frame. This is significant for a SOC as lateral movement is a common tactic used by attackers to expand their access within a network, posing a substantial risk. If confirmed malicious, this activity could allow attackers to escalate privileges, access sensitive information, and persist within the environment, leading to severe security breaches.
data_source: []
search: |-
    | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk
      WHERE All_Risk.analyticstories="Active Directory Lateral Movement" All_Risk.risk_object_type="system"
      BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic
    | `drop_dm_object_name(All_Risk)`
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | where source_count >= 4
    | `active_directory_lateral_movement_identified_filter`
how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased as the analytic story includes over 30 analytics. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
known_false_positives: False positives will most likely be present based on risk scoring and how the organization handles system to system communication. Filter, or modify as needed. In addition to count by analytics, adding a risk score may be useful. In our testing, with 22 events over 30 days, the risk scores ranged from 500 to 80,000. Your organization will be different, monitor and modify as needed.
references:
    - https://attack.mitre.org/tactics/TA0008/
    - https://research.splunk.com/stories/active_directory_lateral_movement/
finding:
    title: Active Directory Lateral Movement Identified - $risk_object$
    entity:
        field: risk_object
        type: system
        score: 0
analytic_story:
    - Active Directory Lateral Movement
asset_type: Endpoint
mitre_attack_id:
    - T1210
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: endpoint
security_domain: endpoint