[Rule Tuning] AWS EFS File System Deleted

[imays11]

Pull Request

Issue link(s):

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

Summary - What I changed

DeleteFileSystem permanently removes an Amazon EFS file system and all stored data. This operation has no recovery path and represents a clear Impact-level destructive action when performed unintentionally or by an unauthorized actor. It is rare in most environments and typically limited to infrastructure teardown or automated provisioning workflows.

Currently this rule also matches DeleteMountTarget events. This action appears frequently in normal EFS lifecycle workflows and is not, by itself, a strong indicator of malicious intent. The only instances of this in telemetry are preceding a DeleteFileSystem event since all attached mount targets must be deleted before deleting a file system (resulting in duplicate alerts for the same destructive behavior). Since only DeleteFileSystem represents irreversible destructive impact, the rule has been narrowed to focus exclusively on the most meaningful threat behavior.

• removed DeleteMountTarget scope from query
• rule name change and toml file name change to match new scope
• reduced execution window
• updated tags
• updated description, FP and IG
• added highlighted fields

How To Test

You can test using this script: trigger_impact_efs_filesytem_deleted.py

Screenshot of expected alert

[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.