[Rule Tunings] AWS New Terms History Window Reduction

[imays11]

Pull Request

Issue link(s):

• https://github.com/elastic/ia-trade-team/issues/616
• [Rule Tuning] Reduce New Term Rules history_window_start #5476

Summary - What I changed

To improve performance, I reduced the history window for new terms rules that were either:

• now-14 days
• showing slow performance metrics

There are still several AWS rules with a now-10d window but they are not showing any performance issues so I'd like to leave them as is for now.

Query Changes

First Time Seen AWS Secret Value Accessed in Secrets Manager

• removed BatchGetSecretValue API call since this calls GetSecretValue
• removed the user_agent exclusions from this one, too easy to bypass.

AWS EC2 User Data Retrieval for EC2 Instance

• excluded more benign AWS services from telemetry

AWS IAM Assume Role Policy Update

• removed use of cloudformation exclusion, this should be captured as well

[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.

[Mikaayenson]

lgtm