[Rule Tuning] Simple KQL to EQL Conversion

Rule: Tuning - Guidelines

These guidelines serve as a reminder set of considerations when tuning an existing rule.

[ ] 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.

[ ] 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.

[ ] 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.