Current compliance monitoring systems for AI don't actually read the rules they're supposed to enforce—they work by pattern matching on scenarios, not rule logic, which undermines their use as regulatory controls.
This paper reveals that compliance detectors used to monitor language models for regulatory violations are 'rule blind'—they make the same decisions regardless of what rule they're supposed to check. The authors show that deleting or swapping rules doesn't change detection accuracy, meaning detectors rely on surface patterns rather than actual rule content.