M365 Copilot Application Usage Pattern Anomalies
SPLUNK detection rule by Rod Soto · Apache-2.0
Detects M365 Copilot users exhibiting suspicious application usage patterns including multi-location access, abnormally high activity volumes, or access to multiple Copilot applications that may indicate account compromise or automated abuse. The detection aggregates M365 Copilot Graph API events per user, calculating metrics like distinct cities/countries accessed, unique IP addresses, number of different Copilot apps used, and average events per day over the observation period. Users are flagged when they access Copilot from multiple cities (cities_count > 1), generate excessive daily activity (events_per_day > 100), or use more than two different Copilot applications (app_count > 2), which are anomalous patterns suggesting credential compromise or bot-driven abuse.
name: M365 Copilot Application Usage Pattern Anomalies
id: e3308b0c-d1a1-40d5-9486-4500f0d34731
version: 4
creation_date: '2025-10-13'
modification_date: '2026-05-13'
author: Rod Soto
status: production
type: Anomaly
description: Detects M365 Copilot users exhibiting suspicious application usage patterns including multi-location access, abnormally high activity volumes, or access to multiple Copilot applications that may indicate account compromise or automated abuse. The detection aggregates M365 Copilot Graph API events per user, calculating metrics like distinct cities/countries accessed, unique IP addresses, number of different Copilot apps used, and average events per day over the observation period. Users are flagged when they access Copilot from multiple cities (cities_count > 1), generate excessive daily activity (events_per_day > 100), or use more than two different Copilot applications (app_count > 2), which are anomalous patterns suggesting credential compromise or bot-driven abuse.
data_source:
- M365 Copilot Graph API
search: >
`m365_copilot_graph_api` (appDisplayName="*Copilot*" OR appDisplayName="M365ChatClient" OR appDisplayName="OfficeAIAppChatCopilot")
| eval user = userPrincipalName
| stats count as events,
dc(location.city) as cities_count,
values(location.city) as city_list,
dc(location.countryOrRegion) as countries_count,
values(location.countryOrRegion) as country_list,
dc(ipAddress) as ip_count,
values(ipAddress) as ip_addresses,
dc(appDisplayName) as app_count,
values(appDisplayName) as apps_used,
dc(resourceDisplayName) as resource_count,
values(resourceDisplayName) as resources_accessed,
min(_time) as first_seen,
max(_time) as last_seen
by user
| eval days_active = round((last_seen - first_seen)/86400, 1)
| eval first_seen = strftime(first_seen, "%Y-%m-%d %H:%M:%S")
| eval last_seen = strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| eval events_per_day = if(days_active > 0, round(events/days_active, 2), events)
| where cities_count > 1 OR events_per_day > 100 OR app_count > 2
| sort -events_per_day, -countries_count
| `m365_copilot_application_usage_pattern_anomalies_filter`
how_to_implement: This detection requires ingesting M365 Copilot access logs via the Splunk Add-on for Microsoft Office 365. Configure the add-on to collect Azure AD Sign-in logs (AuditLogs.SignIns) through the Graph API data input. Ensure proper authentication and permissions are configured to access sign-in audit logs. The `m365_copilot_graph_api` macro should be defined to filter for sourcetype o365:graph:api data containing Copilot application activity.
known_false_positives: Power users, executives with heavy AI workloads, employees traveling for business, users accessing multiple Copilot applications legitimately, or teams using shared corporate accounts across different office locations may trigger false positives.
references:
- https://www.splunk.com/en_us/blog/artificial-intelligence/m365-copilot-log-analysis-splunk.html
drilldown_searches:
- name: View the detection results for "$user$"
search: '%original_detection_search% | search user="$user$"'
earliest_offset: $info_min_time$
latest_offset: $info_max_time$
- name: View risk events for the last 7 days for "$user$"
search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
earliest_offset: 7d
latest_offset: "0"
intermediate_findings:
entities:
- field: user
type: user
score: 20
message: User $user$ exhibited anomalous M365 Copilot usage patterns including multi-location access, excessive activity levels, or multiple application usage indicating potential account compromise or automated abuse.
analytic_story:
- Suspicious Microsoft 365 Copilot Activities
asset_type: Web Application
mitre_attack_id:
- T1078
product:
- Splunk Enterprise
- Splunk Enterprise Security
- Splunk Cloud
category: application
security_domain: endpoint
tests:
- name: True Positive Test
attack_data:
- data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/m365_copilot/m365_copilot_access.log
sourcetype: o365:graph:api
source: AuditLogs.SignIns
test_type: unit
CIRCL