Accountability principle
The GDPR requirement, set out in Article 5(2), that a data controller must be able to demonstrate compliance with the regulation's data protection principles, not merely comply with them silently; an undocumented shadow file holding personal data is out of compliance with this duty for as long as it exists unofficially, whether or not the organization was aware of it.
Defined in 2 GAGE programs, which carry 5 distinct definitions of it. The wording above is taught in Certified AI Data Governance Professional (CADGP).
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.
The GDPR requirement, set out in Article 5(2), that a data controller must be able to demonstrate compliance with the regulation's data protection principles, not merely comply with them silently; an undocumented shadow file holding personal data is out of compliance with this duty for as long as it exists unofficially, whether or not the organization was aware of it.
The data-protection requirement (in the EU GDPR and echoed in laws such as Brazil's LGPD and India's Digital Personal Data Protection Act) that an organization be able to demonstrate, not merely claim, that its processing of personal data is lawful; audit trails are a primary way to meet it.
The rule that an organisation must not only comply with data-protection duties but be able to demonstrate compliance with evidence (GDPR Article 5(2) and equivalents). It is why an undocumented destruction does not count as a completed governance decision.
The GDPR requirement (Article 5(2)) that an organization not only comply with data-protection rules but be able to demonstrate compliance. Provenance you have guessed at cannot be demonstrated; an honest "unknown" can, because it shows you looked, found a gap, and are addressing it.
The rule in the General Data Protection Regulation (GDPR, Regulation (EU) 2016/679), Article 5(2), that a controller must not only comply with the data-protection principles but be able to demonstrate compliance. The legal reason the burden of proof sits on the operator.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The spreadsheet amnesty: ending shadow IT without ending careers · The Humans of Data, Certified AI Data Governance Professional (CADGP)
- The retention decision: what you must keep, what you must destroy, and proving both · Data Reality, Certified AI Governance Professional (CAIGP)
- The provenance file: your data map an auditor could follow · Data Reality, Certified AI Governance Professional (CAIGP)
- The agent audit trail: logging actions so you can reconstruct any decision it made · Agents Under Command, Certified AI Governance Professional (CAIGP)
- Evidence is designed, not gathered: building systems whose proof exists before anyone asks · Evidence Engineering, Certified AI Governance Professional (CAIGP)
Where it shows up at work
A definition is where Accountability principle starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Collects, labels and preserves the evidence that a control operated, a decision was made, and a claim can be defended to an auditor or regulator.
3 lessons on this page teach it as a primary outcome.
Traces where data came from, what transformed it, who owns each hop and where it flows downstream, so a number can be defended.
2 lessons on this page teach it as a primary outcome.
Scopes an AI audit, sets criteria, samples, interviews, tests, writes findings with condition, criteria, cause, effect and recommendation, and tracks remediation.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- AI Auditorneeds it at depth · 9 open postings this week
- Chief Audit Executive, AI audit editionneeds it at depth
- Privacy Analystneeds working knowledge
- Third-Party AI Risk Analystneeds working knowledge
- Senior AI Compliance Analystneeds working knowledge
- AI Compliance Specialistneeds working knowledge · 1 open posting this week
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act Articles 11 and 12; ISO/IEC 42001 Clause 7.5; EU AI Act Article 10; NIST AI RMF MAP 4.
One interview question about it
What evidence would you have ready before an auditor asks about an AI system, and how do you produce it as a byproduct of the work?
Asked of candidates for evidence collection and audit-ready documentation. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Compliance and regulatory affairs carries it as work you can show: Audit-ready documentation is what you produce.
- Internal audit and IT audit carries it as work you can show: Evidence evaluation is the profession.
- Project and program management carries it as a claim to back up: Decision logs and repositories are familiar.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.