GDPR (General Data Protection Regulation)
The European Union's data-protection law, in force since 2018, whose Article 3 reaches organizations outside the EU that offer goods or services to, or monitor, people in the Union. The GDPR established the reach-not-address principle that the AI Act's Article 2 now carries into the AI domain. (Data-protection law is treated in depth elsewhere; it is referenced here for the shared reach logic.)
Defined in 5 GAGE programs, which carry 11 distinct definitions of it. The wording above is taught in Certified AI Governance Professional (CAIGP).
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 European Union's data-protection law, in force since 2018, whose Article 3 reaches organizations outside the EU that offer goods or services to, or monitor, people in the Union. The GDPR established the reach-not-address principle that the AI Act's Article 2 now carries into the AI domain. (Data-protection law is treated in depth elsewhere; it is referenced here for the shared reach logic.)
The European Union's data-protection law, which can require a lawful basis to contact individuals and carries penalties reaching millions of euros or a percentage of global revenue; sets a higher bar than U.S. anti-spam law for prospecting that touches people in covered regions. (Deep treatment in Module 3 and Topic 12.4.)
The European Union's comprehensive data protection law (Regulation (EU) 2016/679, enforceable since May 2018), applying to any organization processing personal data of people in the EU regardless of where the organization is based, and requiring privacy-by-design under Article 25.
The UK and EU data protection law that governs the processing of personal data, including interview notes that identify a named individual and describe their conduct. Requires a lawful basis under Article 6 and adherence to the purpose-limitation principle in Article 5(1)(b).
The European Union's comprehensive data protection law, relevant to any AI system processing EU residents' personal data.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- AI for Business Development: Lead Generation and Prospect Research · Bonus: SMB AI Adoption Path, Certified AI Practitioner: Workplace Foundations
- The interview sweep: what the humans know about the data that the systems do not · The Estate Survey, Certified AI Data Governance Professional (CADGP)
- Lawful basis, spelled out: consent, contract, legitimate interest, and which one actually covers each dataset · Consent, Purpose, and the Law of Data, Certified AI Data Governance Professional (CADGP)
- Article 10 executed: the EU AI Act's data governance duty for the system your organization ships · Lineage Under Audit, Certified AI Data Governance Professional (CADGP)
- Notification decisions: who must be told, when, and in which jurisdiction · Data Incidents, Certified AI Data Governance Professional (CADGP)
- The two gates: is it an AI system, and does the Act apply to your organization at all · The EU AI Act: The Executive Map, Certified AI Governance Professional (CAIGP)
- China, the UK, and the divergence problem: one product, three rulebooks · The World's Rulebooks, 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)
- The DPIA and FRIA, run jointly: one assessment, two regimes, no duplicate work · Evidence Engineering, Certified AI Governance Professional (CAIGP)
- The Liability Risk Assessment and Mitigation Strategy · Legal Frameworks and Liability Navigation, The AI Lobbyist: Certified AI Policy Strategist
Where it shows up at work
A definition is where GDPR (General Data Protection Regulation) 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.
Reviews purpose, data, affected people, accuracy, bias, security, oversight, vendors and law for a use case, scores likelihood and impact, and documents residual risk.
2 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.
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.
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.
- Privacy Analystneeds working knowledge
- AI Privacy Engineerneeds it at depth · 6 open postings this week
- AI Governance Analystneeds working knowledge · 3 open postings this week
- Governance Analystneeds working knowledge · 6 open postings this week
- Third-Party AI Risk Analystneeds working knowledge
- Chief Privacy Officer, AI privacy editionneeds it at depth
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: NIST AI RMF MAP and MEASURE; ISO/IEC 42005; EU AI Act Article 27; EU AI Act Article 10.
One interview question about it
Take me through an AI risk and impact assessment you would run for a hiring tool. What do you assess, and who signs?
Asked of candidates for ai risk and impact assessment. 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.
- Privacy and data protection carries it as work you can show: A DPIA is an impact assessment; the AI version adds accuracy, bias and oversight.
- Data analysis, stewardship and business intelligence carries it as work you can show: Source-to-report lineage is what you trace.
- A data science, statistics or analytics degree carries it as a claim to back up: Lineage and provenance are known ideas, rarely yet documented for an auditor.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.