SEC07-BP04 Define scalable data lifecycle management
Define data lifecycle management that scales, covering how data is handled at ingestion, how sensitivity is reduced through masking or tokenisation, retention periods, provenance tracking and destruction.
5
artefacts
0
held by a system
2
at each review
hard
to go live
Policy repository / GRC workspace
where the evidence lives
teal = a system already holds it · olive = produced at each review
system holds itEvidence a system already holds
none for this control
periodic reviewEvidence produced at each review
- Provenance or lineage records · Document repository
- Evidence of executed deletions · Document repository
governing documentDocuments that govern the control
- Retention schedule per data class · Policy repository / GRC workspace
- Lifecycle policies configured on the storage services · Policy repository / GRC workspace
- Masking or tokenisation design applied near the point of ingestion · Policy repository / GRC workspace
First move
This control is evidenced by people and documents, not systems. Put the document under version control with an owner and review date, and log each review as a record with reviewer and date. Do not try to automate it.
Common gaps auditors find
- data retained indefinitely because no schedule exists
- lifecycle rules configured on some buckets only
- deletion claimed but never evidenced
- sensitivity reduction applied late, after wide copying
Do this for your whole sheet
Paste the rows you run your controls from and get this mapping for every control at once, with the periodic-review ones flagged and a first move per row. No account for the first run.
Build my evidence sheetSEC07-BP03 Automate identification and classification · SEC08-BP01 Implement secure key management