5 standards bodies wrote this down separately. None of them consulted the others. That is not a preference you can argue with, it is what happens.From 5 controls across our corpus
Lithuania Personal Data Protection Law Evidence & Implementation Kit
This is the complete documentation set: an adopt-ready artifact for every control in policy and procedure text you edit rather than draft, and the evidence checklist an auditor asks for against each.
See what is in it, $249
The same set every buyer of this kit receives. Nothing here is produced on request.
Who warns about it
Every framework below independently names this failure. They were written by different
bodies, in different jurisdictions, for different industries, and they agree.
What an auditor asks for
The artefacts named on the controls that warn about this failure. This is what closes
it, and what you will be asked to produce when somebody checks.
- Lawful basis register
- Direct marketing opt-in evidence
- Consent records
- German privacy notices
- Children parental consent (under 16)
- Direct marketing soft-opt-in evidence
What closing it also buys you
The 6 controls that warn about this failure map onto controls in other
frameworks. Close them here and this much of each of those is closed too. It is the same work
counted once, which is usually the difference between a programme that finishes and one that
does not.
Switzerland New Federal Act on Data Protection (nFADP/nDSG, 2023)10
GDPR12
ISO/IEC 29100:20244
ISO/IEC 29134:20233
Bahrain PDPL10
Azerbaijan Law on Personal Data (2010)20
Federal Decree-Law No. 45 of 2021 on the Protection of Personal Data (UAE PDPL)25
South Korea ISMS-P12
APPI10
Armenia Law on Protection of Personal Data (2015)16
Australian Privacy Principles (APPs)23
Barbados Data Protection Act 201913
Scaled against the framework this reaches furthest into, not against a hundred
percent. Exact figures below.
| Also progresses | Covered |
Controls reached |
|---|
| Switzerland New Federal Act on Data Protection (nFADP/nDSG, 2023) Switzerland | 10.7% | 6 of 56 |
| GDPR European Union | 12.5% | 5 of 40 |
| ISO/IEC 29100:2024 International (ISO/IEC JTC 1/SC 27) | 4.5% | 3 of 67 |
| ISO/IEC 29134:2023 International (ISO/IEC JTC 1/SC 27); adopted as CSA ISO/IEC 29134:24 and nationally | 3.5% | 3 of 85 |
| Bahrain PDPL Bahrain | 10.3% | 3 of 29 |
| Azerbaijan Law on Personal Data (2010) Azerbaijan | 20.0% | 3 of 15 |
| Federal Decree-Law No. 45 of 2021 on the Protection of Personal Data (UAE PDPL) United Arab Emirates | 25.0% | 3 of 12 |
| South Korea ISMS-P South Korea | 12.0% | 3 of 25 |
| APPI Japan | 10.0% | 3 of 30 |
| Armenia Law on Protection of Personal Data (2015) Armenia | 16.7% | 3 of 18 |
| Australian Privacy Principles (APPs) Australia | 23.1% | 3 of 13 |
| Barbados Data Protection Act 2019 Barbados | 13.0% | 3 of 23 |
Read as: closing this failure reaches that share of the named
framework's control library through cross-framework mappings held in our corpus. It is not a
claim of compliance with that framework, it is a measure of how much of it you have already
touched.
Where this comes from
Harvested from the control library itself. Every control in our corpus carries the evidence an
auditor expects and the ways implementations commonly fail, recorded when that control was
verified against its source document. This page is those two fields, for one failure, across
every framework that names it.
The overlap is computed by traversing
332,959 cross-framework control mappings out from the specific controls that
warn about this failure, not from the frameworks they sit in. Those mappings were built control
by control against source documents.
Nothing here is inferred, predicted or scored. The number at the top
is a count of frameworks.
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