[Tuning] AWS S3 Unauthenticated Bucket Access by Rare Source

[imays11]

Pull Request

Issue link(s):

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

Summary - What I changed

No query changes as this rule is alerting as expected, however I did change the new terms field to be a combination of an IP address and a particular bucket name. Rather than just alerting for the IP address itself. Perhaps an IP is seen retrieving a doc from a public bucket in the environment (expected behavior) but then it also accesses a file in a bucket meant to be private (unexpected behavior). With new terms only on the IP address we would miss the private bucket access.

• added tls.client.server_name to new terms field (bucket name)
• reduced execution window
• removed duplicate IG
• added setup note for turning on data events
• small edits to description and highlighted fields

How To Test

You can run this script, which will create a new bucket and make an anonymous GetObject call using HTTPS without AWS Credentials. I've changed the rule in our test stack to include the additional bucket name as new terms so it should trigger as expected.

Screenshot of Alert with bucket name highlighted

[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.