[Rule Tuning] Ignore "Not Available" in `o365.audit.UserId` for Microsoft 365 Rules

[terrancedejesus]

Pull Request

Issue link(s):

• https://github.com/elastic/detection-rules/issues/4103
• https://github.com/elastic/ia-trade-team/issues/271

Summary - What I changed

• Ignore Not Available in Microsoft 365 login rules for impossible activity and brute forcing
• Included user only types from Entra ID regarding logins to ignore backend services

How To Test

Testing requires access to the data, however, below is a screenshot of matches from previous emulation. Note that both rules for impossible travel or rare logins are new terms and thresholds, therefore not replicable via Discover to show reduced volume.

Checklist

• [x] 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
• [x] Secret and sensitive material has been managed correctly
• [x] 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.