A transit AI pod keeps sealed-court corpora on-prem while using cloud for public chatbot fine-tunes. Which hybrid placement judgment is correct?

A transit AI pod that keeps sealed-court corpora on-prem while using cloud for public chatbot fine-tunes is exercising correct hybrid placement: keep sensitive sealed-court data on-prem and allow non-sensitive public fine-tunes in cloud. Placement follows sensitivity labels—restricted corpora stay inside the municipal boundary; eligible public workloads may consume cloud capacity. Keeping sealed-court corpora on-prem and sharing anonymized sealed embeddings for cloud fine-tunes still places sealed-court-derived material outside the on-prem boundary, and anonymization alone does not make it eligible for cloud placement. Keeping sealed-court jobs on-prem and running public fine-tunes on that cluster off-peak with isolation still diverts restricted infrastructure to non-sensitive work, and isolation does not make it appropriate when eligible public jobs may use the cloud. Keeping sealed-court data on-prem and routing cloud bursting by latency with tags logged for audit puts performance ahead of classification; tags logged for audit do not override the rule that sealed-court data stays on-prem. Exam caveat: hybrid architecture is a split by data and workload sensitivity, not an all-cloud or all-on-prem slogan; mis-labeling sealed content as "public enough" is a placement error even if embeddings are anonymized. Operational check: tag transit datasets by sensitivity, enforce placement policy so sealed-court objects never select cloud storage classes, and allow only labeled non-sensitive fine-tune jobs to target cloud trainers.