# Unusual Number of Modifications to Cloud ACLs

# Unusual Number of Modifications to Cloud ACLs

## Description

Looks for a large number of Security Group ACL changes in a short period of time for a user.

## Content Mapping

This content is not mapped to any local saved search. Add mapping

## 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 a spike in the number we're monitoring... it's nearly impossible for the math to lie. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise. How you handle these alerts depends on where you set the standard deviation. If you set a low standard deviation (2 or 3), you are likely to get a lot of events that are useful only for contextual information. If you set a high standard deviation (6 or 10), the amount of noise can be reduced enough to send an alert directly to analysts. If your orchestration tools will perform these actions, and they aren't a part of the baseline (or they're very rare in the baseline), those could create false positives. You might opt to whitelist those tools, or alternatively to make a part of the response verifying in your change management whether those tools were scheduled. |

## How To Respond |
---|

When this alert fires, you should call the user and see if they expected this behavior. If the user cannot attribute this activity, it is best to reset the keys and continue your investigation to see what occurred. In particular, look to see what the ACL changes were, to see what new access has been allowed. Check your VPC Flow logs to see if data was sent over those newly allowed connections. |

## Help |
---|

## Unusual Number of Modifications to Cloud ACLs HelpThis example leverages the Detect Spikes 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, GCP and Azure Audit, as detailed in How to Implement. |

## SPL for Unusual Number of Modifications to Cloud ACLs

### 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. |

| With our dataset onboard, we then filter down to just the events indicating a modification of ACLs |

| Bucket (aliased to bin) allows us to group events based on _time, effectively flattening the actual _time value to the same day. |

| Next we use stats to summarize the number of events per user per day. |

| calculate the mean, standard deviation and most recent value |

| calculate the bounds as a multiple of the standard deviation |

### AWS Data

| First we bring in our basic dataset, AWS CloudTrail logs that are filtered for ACL modification events. |

| Bucket (aliased to bin) allows us to group events based on _time, effectively flattening the actual _time value to the same day. |

| Next we use stats to summarize the number of events per user per day. |

| calculate the mean, standard deviation and most recent value |

| calculate the bounds as a multiple of the standard deviation |

### GCP Data

| First we bring in our GCP Audit logs, filtered for ACL modification. |

| Bucket (aliased to bin) allows us to group events based on _time, effectively flattening the actual _time value to the same day. |

| Next we use stats to summarize the number of events per user per day. |

| calculate the mean, standard deviation and most recent value |

| calculate the bounds as a multiple of the standard deviation |

### Azure Data

| First we bring in our Azure Audit logs filtered for ACL modification. |

| |

| Next we use stats to summarize the number of events per user per day. |

| calculate the mean, standard deviation and most recent value |

| calculate the bounds as a multiple of the standard deviation |