Implement centralized logging, defined retention schedules, and continuous monitoring for the full data lifecycle is the correct option because it establishes an end to end governance foundation that supports auditability and regulatory proof across collection, storage, processing, model training, and deletion.

Implement centralized logging, defined retention schedules, and continuous monitoring for the full data lifecycle creates immutable audit trails and consistent retention enforcement, and it enables automated alerts for misuse or policy drift so the organization can demonstrate compliance during audits and investigations.

Restrict developer access to training data with IAM roles is important for applying least privilege, but it does not by itself provide lifecycle controls, comprehensive logging, or organization wide retention policies that prove how long data was kept and when it was removed.

Anonymize every dataset before model training can reduce exposure of sensitive attributes, but anonymization alone does not provide data lineage, retention records, or continuous monitoring and some models require partially identifiable features for correct behavior.

AWS Lake Formation helps with permissions and cataloging inside data lakes, but it is a component level service and does not by itself deliver centralized, organization wide logging, retention scheduling, and continuous monitoring across all data sources and services.

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