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@imays11 imays11 commented Nov 5, 2025

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Summary - What I changed

No hits in telemetry for this rule yet. Which is good as it is extremely rare and high-risk behavior for an EC2 instance to exhibit any console login behavior.

  • used event.type as event_category_override field to remove use of any in query
  • updated description and investigation guide
  • updated tags
  • updated Mitre mapping
  • added highlighted fields

How To Test

We have data available in our stack to run the query against
Script for triggering the rule : trigger_lateral_movement_ec2_instance_console_login.py

Screenshots of expected alert and working query with event.type as event category override field

Screenshot 2025-11-05 at 4 58 57 PM Screenshot 2025-11-05 at 4 55 02 PM

No hits in telemetry for this rule yet. Which is good as it is extremely rare and high-risk behavior for an EC2 instance to exhibit any console login behavior.
- used `event.type` as event_category_override field to remove use of `any` in query
- updated description and investigation guide
- updated tags
- updated Mitre mapping
- added highlighted fields
normalized Sign-In tag
@imays11 imays11 self-assigned this Nov 5, 2025
@imays11 imays11 added Integration: AWS AWS related rules Rule: Tuning tweaking or tuning an existing rule Team: TRADE Domain: Cloud labels Nov 5, 2025
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github-actions bot commented Nov 5, 2025

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.

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Domain: Cloud Integration: AWS AWS related rules Rule: Tuning tweaking or tuning an existing rule Team: TRADE

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