Skip to content

Conversation

@shashank-elastic
Copy link
Contributor

Pull Request

Issue link(s): Double Version Bumps seen in version lock #4212.

Similar fix to #4156

Summary - What I changed

  • Update Min stack version for windows integration

How To Test

  • Unit test to pass

Checklist

  • Added a label for the type of pr: bug, enhancement, schema, Rule: New, Rule: Deprecation, Rule: Tuning, Hunt: New, or Hunt: Tuning so guidelines can be generated
  • Added the meta:rapid-merge label if planning to merge within 24 hours
  • Secret and sensitive material has been managed correctly
  • Automated testing was updated or added to match the most common scenarios
  • Documentation and comments were added for features that require explanation

Contributor checklist

@shashank-elastic shashank-elastic added Rule: Tuning tweaking or tuning an existing rule meta:rapid-merge labels Oct 28, 2024
@shashank-elastic shashank-elastic self-assigned this Oct 28, 2024
@botelastic botelastic bot added bbr Building Block Rules Domain: Endpoint ML machine learning related rule OS: Windows windows related rules labels Oct 28, 2024
@protectionsmachine
Copy link
Collaborator

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 shashank-elastic merged commit 92fe46b into main Oct 28, 2024
38 checks passed
@shashank-elastic shashank-elastic deleted the fix_version_bump_windows branch October 28, 2024 13:58
protectionsmachine pushed a commit that referenced this pull request Oct 28, 2024
protectionsmachine pushed a commit that referenced this pull request Oct 28, 2024
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

backport: auto bbr Building Block Rules Domain: Endpoint meta:rapid-merge ML machine learning related rule OS: Windows windows related rules Rule: Tuning tweaking or tuning an existing rule

Projects

None yet

Development

Successfully merging this pull request may close these issues.

6 participants