[Rule Tuning] AWS Access Token Used from Multiple Addresses

[imays11]

Pull Request

Issue link(s):

• https://github.com/elastic/ia-trade-team/issues/616
• [Rule Tuning] AWS Access Token Used from Multiple Addresses - possible Defender for Cloud Apps #5205

Summary - What I changed

This rule is extremely loud in telemetry ~2612 alerts in last 24 hours. There have also been a couple community requests for changes.

• reduced the scope of the alerts to only surface the "high" fidelity_score cases for "multiple_ip_network_city" or "multiple_ip_network_city_user_agent" criteria. This reduced telemetry by ~90%
• excluded 2 more benign service providers support which reduced volume by another 6%.
• added the data_stream.namespace field as requested.
• kept the rest of the rule logic visible so that if customers would like to broaden the scope of this rule again, they can duplicate the rules and revert back to the broader condition Esql.activity_type != "normal_activity". This has been included as a comment in the rule query.

I will keep an eye on this rule in telemetry to determine it's value moving forward.

[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.