Cloud Provisioning Activity from Unusual IP

Description

Looks for Cloud Provisioning activities that occur from new IPs (for organizations with strict IP controls).


Use Case

Advanced Threat Detection

Category

Account Compromise, IAM Analytics, Account Sharing, SaaS

Security Impact

The risk that this detection intends to reduce is the compromise of a cloud environment, where all of a sudden provisioning occurs from IP Addresses that have not been seen before (only applicable for organizations that have strict policies around IP Addresses for access). Assuming that the user is not traveling, and that new orchestration tools are not being used, this would suggest that credentials have been created or compromised, and are in control of an adversary. This could result in potential data leakage, data deletion, or cost run-up.

Alert Volume

High (?)

SPL Difficulty

Medium

Journey

Stage 3

MITRE ATT&CK Tactics

Persistence
Privilege Escalation

MITRE ATT&CK Techniques

Valid Accounts

MITRE Threat Groups

APT18
APT28
APT3
APT32
APT33
APT39
APT41
Carbanak
Dragonfly 2.0
FIN10
FIN4
FIN5
FIN6
FIN8
Leviathan
Night Dragon
OilRig
PittyTiger
Soft Cell
Stolen Pencil
Suckfly
TEMP.Veles
Threat Group-1314
Threat Group-3390
menuPass

Kill Chain Phases

Actions on Objectives

Data Sources

Audit Trail
GCP
Azure
AWS

   How to Implement

Assuming you use the ubiquitous AWS, GCP, or Azure Add-ons for Splunk to pull these logs in, this search should work automatically for you without issue. While implementing, make sure you follow the best practice of specifying the index for your data.

   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 over (or for the lookup cache feature, the first occurrence over whatever time period you built the lookup). But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.

For most organizations, this search is not going to be useful apart from contextual data (i.e., when we are analyzing another alert related to account compromise, we can also see that this user logged in from an unusual IP). The reason is that most organizations don't apply strict controls to what IPs are allowed to reach out to their Cloud orchestration environments. However, if your organization does have strict controls, and all of a sudden a new IP shows up, that could be very noteworthy.

   How To Respond

For organizations that have strict allowed IPs for cloud orchestration, you may opt to use Splunk's Adaptive Response Actions to automatically disable an account that is doing provisioning from a new IP. More commonly, the concern is account compromise so it is prudent to immediately call the user and find out if they intended to take those actions.

   Help

Cloud Provisioning Activity from Unusual IP Help

This example leverages the Detect New Values search assistant. Our example dataset is a collection of anonymized AWS CloudTrail logs, during which someone does something bad. Our live search looks for the same behavior using the very standardized index and sourcetypes for AWS CloudTrail, Azure and GCP Audit, as detailed in How to Implement.

SPL for Cloud Provisioning Activity from Unusual IP

Demo Data

First we bring in our basic demo dataset. In this case, anonymized AWS CloudTrail logs. We're using a macro called Load_Sample_Log_Data to wrap around | inputlookup, just so it is cleaner for the demo data.
Then we filter for provisioning activities (somewhat broadly).
Here we use the stats command to calculate what the earliest and the latest time is that we have seen this combination of fields.
Next we calculate the most recent value in our demo dataset
We end by seeing if the earliest time we've seen this value is within the last day of the end of our demo dataset.

AWS Data

First we bring in our basic demo dataset. In this case, AWS CloudTrail logs filtered for provisioning activities.
Here we use the stats command to calculate what the earliest and the latest time is that we have seen this combination of fields.
We end by seeing if the earliest time we've seen this value is within the last day.

GCP Data

First we bring in our GCP Audit logs, filtered for instance creation or modification.
Here we use the stats command to calculate what the earliest and the latest time is that we have seen this combination of fields.
We end by seeing if the earliest time we've seen this value is within the last day.

Azure Data

First we bring in our Azure Audit logs, filtered for instance creation or modification.
Here we use the stats command to calculate what the earliest and the latest time is that we have seen this combination of fields.
We end by seeing if the earliest time we've seen this value is within the last day.

Screenshot of Demo Data