[Rule Tuning] AWS SSM SendCommand Execution by Rare User

[imays11]

Pull Request

Issue link(s):

• https://github.com/elastic/ia-trade-team/issues/616

Summary - What I changed

Rule is triggering as expected, very low instances of alerts in telemetry

• adjusted execution window
• slight edits to IG for accuracy
• removed exclusion and not aws.cloudtrail.user_identity.arn: *AWSServiceRoleForAmazonSSM/StateManagerService* from the query. This is a service-linked role meant to be used by AWS internal services. Therefore, the existing exclusion and not source.address: "ssm.amazonaws.com" already excludes the use of this role by the SSM service. I show this in the screenshot below. This will remove the use of wildcards in the query and improve performance.
• changed the new terms fields to use combination of cloud.account.id and user.name so that only roles (and not individual role sessions) are being evaluated. adding cloud.account.id accounts for duplicate user.names across multiple accounts.

How To Test

You can use this script for testing, it will create an EC2 instance and a user along with access keys. Then use those keys to call ssm sendcommand. These are the steps you need to take to manually trigger this rule.

[github-actions]

Rule: Tuning - Guidelines

These guidelines serve as a reminder set of considerations when tuning an existing rule.

Documentation and Context

• Detailed description of the suggested changes.
• Provide example JSON data or screenshots.
• Provide evidence of reducing benign events mistakenly identified as threats (False Positives).
• Provide evidence of enhancing detection of true threats that were previously missed (False Negatives).
• Provide evidence of optimizing resource consumption and execution time of detection rules (Performance).
• Provide evidence of specific environment factors influencing customized rule tuning (Contextual Tuning).
• Provide evidence of improvements made by modifying sensitivity by changing alert triggering thresholds (Threshold Adjustments).
• Provide evidence of refining rules to better detect deviations from typical behavior (Behavioral Tuning).
• Provide evidence of improvements of adjusting rules based on time-based patterns (Temporal Tuning).
• Provide reasoning of adjusting priority or severity levels of alerts (Severity Tuning).
• Provide evidence of improving quality integrity of our data used by detection rules (Data Quality).
• Ensure the tuning includes necessary updates to the release documentation and versioning.

Rule Metadata Checks

• updated_date matches the date of tuning PR merged.
• min_stack_version should support the widest stack versions.
• name and description should be descriptive and not include typos.
• query should be inclusive, not overly exclusive. Review to ensure the original intent of the rule is maintained.

Testing and Validation

• Validate that the tuned rule's performance is satisfactory and does not negatively impact the stack.
• Ensure that the tuned rule has a low false positive rate.