AI pipelines undergo frequent changes, and each change can trigger a full set of governance and assurance checks. Re-running everything is expensive, so teams often reuse earlier evidence. A new arXiv preprint argues that such reuse is only safe if the dependencies declared by each control cover all relevant changes.
The authors propose explicit validity conditions for reusing assurance evidence, plus a fail-closed release gate that blocks releases when conditions are not met. They also report a replay of 714 public data version control pipelines to examine how the approach behaves in practice.
The paper is from arXiv (2610.27672v1). The abstract is truncated, so details on the replay results are not fully available in the source. The work addresses a practical tension between cost and safety in AI governance.