Explainability Requirement

Recovering training data from gradients.

Inferring sensitive features of training data.

Extracting system prompts or hidden instructions.

Compromising AI systems via libraries, models, or datasets.

Central catalog of deployed and experimental models.

Categorizing AI applications by impact and regulatory risk.

Required human review for high-risk decisions.

Logged record of model inputs, outputs, and decisions.