[Rule Tuning] Entra ID OAuth Device Code Phishing via AiTM

[terrancedejesus]

Pull Request

Issue link(s):

• https://github.com/elastic/ia-trade-team/issues/907
• Entra ID OAuth Device Code Phishing via AiTM wrong resource id #6338

Summary - What I changed

Fixes #6338 and replaces 00000005-0000-0ff1-ce00-000000000000 with resource ID for Microsoft Graph 00000003-0000-0000-c000-000000000000.

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.

[Copilot]

Pull request overview

This PR tunes an existing Azure/Entra ID sign-in detection rule to align the resource_id filter with the intended Microsoft Graph target (per #6338) and updates the rule’s metadata date accordingly.

Changes:

• Update the rule’s updated_date to reflect the tuning.
• Replace the previously used resource ID with Microsoft Graph’s resource ID in the KQL query filter.