[Rule Tunings] AWS Role Assumption By Service / User

[imays11]

Pull Request

Issue link(s):
https://github.com/elastic/ia-trade-team/issues/616

Summary - What I changed

AWS Role Assumption By Service
The newest versions of this rule seem fine in telemetry and the rule executes as expected

• removed MD from description
• adjusted execution window for 1 m look back
• fixed inaccuracies in Investigation Guide
• added Lateral Movement tag
• adjusted highlighted fields
• reduced history window from 14 to 10 days

AWS Role Assumption By User
This rule seem fine in telemetry and the rule executes as expected

• removed MD from description
• fixed inaccuracies in Investigation Guide
• added Lateral Movement tag
• adjusted highlighted fields
• added cloud.account.id to new_terms field to account for duplicate user.names across cloud accounts
• replaced new terms flattened field for aws.cloudtrail.resources.arn, which gives the same result and remains consistent with the other rule.

How To Test

script for AWS Role Assumption by Service

script for AWS Role Assumption by User

To test either of these rules manually you'll need to use the AssumeRole API to assume a role using either a role, a user account, or a service like EC2 or Lambda. All of these will need a trust policy attached to the target role you're attempting to assume which allows for your user or service to assume it. You'll also need to attach an inline policy to your user account, role or service to allow the sts:AssumeRole action against the target role. The test scripts set up new roles and all the appropriate trust policies and IAM policies.

[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.