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The following analytic detects cloud provisioning activities originating from previously unseen cities. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from an unexpected location. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Change
  • Last Updated: 2024-05-16
  • Author: Rico Valdez, Bhavin Patel, Splunk
  • ID: e7ecc5e0-88df-48b9-91af-51104c68f02f




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.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| iplocation src 
| where isnotnull(City) 
| lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenCity=min(firstTimeSeen) 
| where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) 
| `security_content_ctime(firstTime)` 
| table firstTime, src, City, user, object, command 
| `cloud_provisioning_activity_from_previously_unseen_city_filter`


The SPL above uses the following Macros:

:information_source: cloud_provisioning_activity_from_previously_unseen_city_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.action
  • All_Changes.status
  • All_Changes.src
  • All_Changes.user
  • All_Changes.object
  • All_Changes.command

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud Provisioning Activity Sources - Initial to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search Previously Seen Cloud Provisioning Activity Sources - 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 previously_unseen_cloud_provisioning_activity_window macro. You can also provide additional filtering for this search by customizing the cloud_provisioning_activity_from_previously_unseen_city_filter macro.

Known False Positives

This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise. This search will fire any time a new IP address is seen in the GeoIP database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of MaxMind GeoIP that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.

Associated Analytic Story


Risk Score Impact Confidence Message
18.0 30 60 User $user$ is starting or creating an instance $object$ for the first time in City $City$ from IP address $src$

: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 tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

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