[Rule Tuning] Entra ID User Sign-in with Unusual Client

[terrancedejesus]

Fixes #5472

Pull Request

Issue link(s):

• [Rule Tuning] Entra ID User Sign-in with Unusual Client #5472

Summary - What I changed

Tunes the Entra ID User Sign-in with Unusual Client rule to reduce BAU FPs from first-party client IDs. Please see the issue for more details. Additionally, investigation fields were added.

How To Test

• Query can be used in telemetry stack (1+ years).

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?

[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]

you may also want to update the investigation guide to better match the query being more about non-interactive auth.

[Mikaayenson]

Suggested change

	- Exlcude known custom applications from `azure.signinlogs.properties.app_id` that are authorized to use non-interactive authentication.
	- Exclude known custom applications from `azure.signinlogs.properties.app_id` that are authorized to use non-interactive authentication.

[Mikaayenson]

There are duplicates like 9ea1ad79-fdb6-4f9a-8bc3-2b70f96e34c7 that should be removed in this list.