[Rule Tuning] Update Okta and Github Min-Stack Versions for Release

[terrancedejesus]

Pull Request

Issue link(s): As part of double bumps seen in #4289

Summary - What I changed

• Minstack Github to 8.13.0

For versions v8.13.0 and above

  "related_integrations": [
    {
      "package": "github",
      "version": "^2.0.0"
    }
  ],

For versions v8.12.0 and below

  "related_integrations": [
  {
    "package": "github",
    "version": "^1.0.0"
  }
],

• Minstack Okta to 8.15.0

For versions v8.15.0 and above

  "related_integrations": [
    {
      "package": "okta",
      "version": "^3.0.0"
    }
  ],

For versions v8.14.0 and below

  "related_integrations": [
  {
    "package": "okta",
    "version": "^2.0.0"
  }
],

How To Test

Checklist

• Added a label for the type of pr: bug, enhancement, schema, maintenance, 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

• Have you signed the contributor license agreement?
• Have you followed the contributor guidelines?

[protectionsmachine]

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.

[eric-forte-elastic]

🟢 Manual review, looks good to me once unit tests pass! 👍

Context PR

[shashank-elastic]

LGTM with unit test passing we can merge