[Rule Tuning] AWS EC2 AMI Shared with Another Account

[imays11]

Pull Request

Issue link(s):

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

Summary - What I changed

• rule is executing as expected and showing low alert volume in telemetry
• false positive exclusion added for AWS marketplace which invokes this API without user-interaction as part of normal operations
• triage notes have been included to verify legitimacy of other Service APIs that invoke this API call but due to user-initiated sharing requests. They should be investigated as well.
• adjusted execution window
• removed unnecessary wildcard queries in place of only one add attribute
• this now includes attempts to share AMIs with individual accounts (userId=123456789012), organizations (organizationArn=arn:*), and to make AMIs public (group=all)

How To Test

You can test this using the following script

Or manually by attempting to share an AMI image with another account, organization arn, or make it public

aws ec2 modify-image-attribute \
    --image-id ami-5731123e \
    --launch-permission "Add=[{Group=all}]" 

[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.

[shashank-elastic]

The tuning descriptions are 🚀 Makes review context so simple. Great Job @imays11