[Rule Tuning] AWS S3 Bucket Expiration Lifecycle Configuration Added

[imays11]

Pull Request

Issue link(s):

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

Summary - What I changed

AWS S3 Bucket Expiration Lifecycle Configuration Added
No major telemetry concerns for this rule's execution.

• changed rule type to EQL so as not to use the double wildcard
• used event.type as event category override field because event.category is not mapped for PutBucketLifecycle action
• removed unnecessary *LifecycleConfiguration* check from query, this field is required for any PutBucketLifecycle API call so unnecessary to include in the query.
• updated description and IG
• reduced execution window
• updated Mitre mapping
• removed incorrect setup notes
• added highlighted fields

NOTE: While PutBucketLifecycle is deprecated and replaced by PutBucketLifecycleConfiguration both APIs are still supported and both show in Cloudtrail as PutBucketLifecycle which is why that is the only call in the query.

How To Test

• Script for executing
• Plenty of data in our test stack for running query against

Screenshot of new working query (with event.type as event category)

[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.