Skip to content

Conversation

@imays11
Copy link
Contributor

@imays11 imays11 commented Nov 5, 2025

Pull Request

Issue link(s):

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
@github-actions
Copy link
Contributor

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.

Copy link
Contributor

@terrancedejesus terrancedejesus left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Telem hunting rule :)

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

Projects

None yet

Development

Successfully merging this pull request may close these issues.

4 participants