Author a Python detection rule using Panther's pypanther framework and deploy it as versioned detection-as-code

Steps

1. Define a rule as a single Python class using pypanther, Panther's current Python-native detection framework (which superseded its earlier YAML-plus-Python rule format), specifying the applicable log type(s) and a rule() method returning true or false.
2. Set severity, tags, and other metadata as class-level attributes, and extend or override built-in base rules via inheritance rather than duplicating logic.
3. Add unit tests with sample log events, including both positive and negative cases, alongside the rule so CI validates detection logic before deployment.
4. Deploy the rule set to the Panther backend through the pypanther CLI/CI pipeline, keeping the rules repository in git so every detection change goes through pull-request review and is revertible.
5. Apply the same detection-as-code discipline — rules in git, PR review, unit tests, CI-driven deploy — on any other log-pipeline platform in this space, adapting to that platform's specific rule authoring format.