I would not infer rights from the word “derived.” We need the actual agreement, its term, permitted uses, survival clauses, deletion duties, geography and treatment of derivatives. Training permission, continued model use, retention for audit, retrieval display and redistribution can have different terms. A Creative Commons license is different again. Creative Commons explains why AI training rights do not follow a single universal rule. This answer is an engineering decision process. The legal conclusion needs the agreement and qualified review.

For the first day, prevent fresh uses we already know may be unauthorized. Stop new training and ingestion from the source. Disable retrieval of its text and derivative snippets while ownership checks run, including cached passages and precomputed summaries that quote it. Preserve narrowly controlled evidence of what was used and when, if the agreement and retention obligations allow. Do not erase the only lineage that could identify affected assets before we have a defensible remediation plan. Put the trained model or adapter behind a release decision, with a scoped hold if the continued-serving right is unclear. A blanket answer of “delete weights” might be impossible or unnecessary. A blanket answer of “weights are safe” is just as unsupported.

The lineage graph needs stable source IDs and license versions, not a spreadsheet saying “partner dataset.” For each document or batch, record the agreement reference, acquisition date, permitted purposes and effective dates. Connect it to transformations, deduplication, training runs, checkpoints, fine-tunes, eval sets, embeddings, indexes, caches and serving artifacts. SageMaker lineage tracking illustrates how data preparation, models and deployment can be connected. It does not decide license rights for us.

Now the hard question: can we remove the data's influence from a base model? Deleting source files or a vector index does not reverse a completed training run. An adapter might be separable if we can prove exactly which artifact used the corpus. A base model trained on a mixed set may require retraining from a clean checkpoint, a negotiated right to continue, or withdrawal, depending on the agreement and risk. Machine unlearning claims would need evidence tied to this obligation. We should not promise that blocking a few verbatim generations makes the rights issue disappear.

Separate already delivered responses from future serving. Whether copies need takedown, notice or preservation depends on the actual terms and other duties. What we can do immediately is identify affected products and customers through provenance, prevent further disclosure where required, and retain a traceable record of the decision. Also check whether the retrieval corpus includes later independent sources of the same facts. A license on one expression does not automatically make every fact inaccessible, but that is another contract and rights judgment, not a shortcut for engineers.

The platform gate I would want is purpose-aware. Before a dataset is admitted to training, eval or retrieval, it must have a current grant for that purpose. A change in grant produces an impact report over downstream artifacts and a serving policy decision with an owner and deadline. Tenant traces become a shared evaluation dataset deals with tenant traces leaking into a shared eval set. This case asks what happens when permission was initially valid but changes after multiple kinds of derived assets already exist.