[Rule Tunings] AWS IAM Roles Anywhere Rules

[imays11]

Pull Request

Issue link(s):

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

Summary - What I changed

Both these rules have low volume in telemetry as expected, this is quite rare behavior. No major changes to the rule logic itself.

AWS IAM Roles Anywhere Profile Creation

• updated description and investigation guide
• reduced execution window
• added highlighted fields

AWS IAM Roles Anywhere Trust Anchor Created with External CA

• changed rule type to EQL to use stringContains instead of leading wildcard
• uses event.type as event category override field
• reduced execution window
• updated description and investigation guide
• added highlighted fields

How To Test

Data in our shared stack to run the query against and the following scripts to trigger the rules:

• trigger_persistence_iam_roles_anywhere_profile_created.py
• trigger_persistence_iam_roles_anywhere_trusted_anchor_created_with_external_ca.py

Screenshot of Both expected rule Alerts

Screenshot of updated eql query for Trust Anchor 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.