The Defensibility Index replaces standard human-agreement metrics to better evaluate rule-governed AI systems. Traditional benchmarks often penalize logically consistent decisions that differ from a single human label. This research introduces Probabilistic Defensibility Signals using token logprobs. Practitioners can now distinguish between actual model errors and valid policy interpretations during audits.