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@imays11 imays11 commented Jun 17, 2025

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Summary - What I changed

  • changed execution window
  • explicitly added flattened fields to query, to reduce wildcard usage
  • added investigation fields
  • changed new terms field to evaluate user.name over aws.cloudtrail.user_identity.arn so that only the role name for Assumed Role identitites is being evaluated instead of each individual session. This should greatly impact performance as most instances of this rule in telemetry is triggered by Assumed Roles.
Screenshot 2025-06-17 at 3 16 16 AM Screenshot 2025-06-17 at 3 15 04 AM

How To Test

You can use this script to test the rule using both an IAM user and a Role

To test manually launch an EC2 instance and use your user or role to access it's user data with the following command:

aws ec2 describe-instance-attribute --instance-id i-1234567890abcdef0 --attribute userData 
- changed execution window - explicitly added flattened fields to query, to reduce wildcard usage - added investigation fields - changed new terms field to evaluate `user.name` over `aws.cloudtrail.user_identity.arn` so that only the role name for Assumed Role identitites is being evaluated instead of each individual session. This should greatly impact performance as most instances of this rule in telemetry is triggered by Assumed Roles.
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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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@Aegrah Aegrah left a comment

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LGTM!

"""
from = "now-9m"
from = "now-6m"
interval = "5m"
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IINM, may not need to directly add the default interval.

imays11 and others added 2 commits June 17, 2025 14:23
Co-authored-by: Mika Ayenson, PhD <Mikaayenson@users.noreply.github.com>
@imays11 imays11 merged commit 74d1715 into main Jun 17, 2025
14 checks passed
@imays11 imays11 deleted the tune_ec2_data_retrieval branch June 17, 2025 18:51
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