This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

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The following analytic detects when a new command is run by a user, who typically does not run those commands. The detection is made by a Splunk query to search for these commands in the Change data model. Identifies commands run by users with the user_type of AssumedRole and a status of success. The query retrieves the earliest and latest timestamps of each command run and groups the results by the user and command. Then, it drops the unnecessary data model object name and creates a lookup to verify if the command was seen before. The lookup table contains information about previously seen cloud API calls for each user role, including the first time the command was seen and whether enough data is available for analysis. If the firstTimeSeenUserApiCall field is null or greater than the relative time of 24 hours ago, it indicates that the command is new and was not seen before. The final result table includes the firstTime, user, object, and command fields of the new commands. It also applies the security_content_ctime function to format the timestamps and applies a filter to remove any cloud API calls from previously unseen user roles. The detection is important because it helps to identify new commands run by different user roles. New commands can indicate potential malicious activity or unauthorized actions within the environment. Detecting and investigating these new commands can help identify and mitigate potential security threats earlier, preventing data breaches, unauthorized access, or other damaging outcomes.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Change
  • Last Updated: 2020-09-04
  • Author: David Dorsey, Splunk
  • ID: 2181ad1f-1e73-4d0c-9780-e8880482a08f




ID Technique Tactic
T1078 Valid Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
Kill Chain Phase
  • Exploitation
  • Installation
  • Delivery
  • DE.AE
  • CIS 10
| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenUserApiCall=min(firstTimeSeen) 
| where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") 
| table firstTime, user, object, command 
| `security_content_ctime(lastTime)`
| `cloud_api_calls_from_previously_unseen_user_roles_filter`


The SPL above uses the following Macros:

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


The SPL above uses the following Lookups:

Required fields

List of fields required to use this analytic.

  • _time
  • All_Changes.user
  • All_Changes.user_type
  • All_Changes.status
  • All_Changes.command
  • All_Changes.object

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud API Calls Per User Role - Initial to build the initial table of user roles, commands, and times. You must also enable the second baseline search Previously Seen Cloud API Calls Per User Role - Update to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the cloud_api_calls_from_previously_unseen_user_roles_activity_window macro. You can also provide additional filtering for this search by customizing the cloud_api_calls_from_previously_unseen_user_roles_filter

Known False Positives


Associated Analytic Story


Risk Score Impact Confidence Message
36.0 60 60 User $user$ of type AssumedRole attempting to execute new API calls $command$ that have not been seen before

: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.


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

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