GLOSSARY

An international standard defining the stages, transitions, and governance processes of the data lifecycle for artificial intelligence applications.

Clear data lifecycle stages ensure data lineage, IP provenance, privacy compliance, and defensible audit trails throughout AI training and fine-tuning pipelines.

An enterprise data team maps its AI pipeline to the ISO/IEC 8183 stages to enforce role-based access, retention policies, and cryptographic provenance from data ingestion to model deprecation.

A lifecycle framework outlines process stages and transitions, but relies on organizational enforcement and integrated technical tooling to prevent data drift or compliance lapses.

ISO/IEC 8183 defines the end-to-end data life cycle tailored for AI development. It formalizes stages including data acquisition, creation, preparation, processing, storage, sharing, use, retention, and disposal. The standard specifies activities, inputs, outputs, and governance checkpoints required at each phase to maintain data integrity and traceability.