Retention period
A documented, specific duration (or a defined trigger event and duration) after which a category of personal data must be deleted, anonymized, or moved into a lawful archiving exemption. The absence of a documented retention period is itself treated by regulators as a failure, not a neutral default.
Defined in 4 GAGE programs, which carry 6 distinct definitions of it. The wording above is taught in AI Data Governance: The Data Chair.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A documented, specific duration (or a defined trigger event and duration) after which a category of personal data must be deleted, anonymized, or moved into a lawful archiving exemption. The absence of a documented retention period is itself treated by regulators as a failure, not a neutral default.
The length of time the trail is kept before deletion, set as a decision that balances the record-keeping duty (a legal minimum, for example at least six months for high-risk systems under the EU AI Act) against the privacy cost of holding personal data longer than needed.
The length of time an organization keeps an audit record, disclosure form, or gate score after the underlying content is taken down or the audit cycle closes, set long enough to cover a realistic window for a regulator to request historical evidence.
The length of time audit-trail records must be kept, set to match the longest relevant record-keeping rule and statute of limitations. A log deleted before its retention period expires cannot be produced when discovery arrives.
The length of time a specific dataset or field-group may be kept, derived from its purpose and any overriding keep obligation, not chosen for convenience.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- The "we have always had this data" trap: age does not launder a dataset · Consent, Purpose, and the Law of Data, AI Data Governance: The Data Chair
- The retention decision: what you must keep, what you must destroy, and proving both · Data Reality, AI Governance: Applied Mastery
- The agent audit trail: logging actions so you can reconstruct any decision it made · Agents Under Command, AI Governance: Applied Mastery
- The logging architecture: what your organization's systems must record, for whom, for how long · Evidence Engineering, AI Governance: Applied Mastery
- Building the Audit-Ready AI Program · Lab: Regulated Industries, Business AI Transformation
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.