Data minimization
The GDPR principle (Article 5(1)(c)) and general good-practice standard requiring that only the personal data necessary for a stated purpose be collected and retained. Applied to vector stores, this means storing a non-identifying reference rather than raw personal data in the payload field wherever the application's functionality does not strictly require the raw data to be present in that specific system.
Defined in 4 GAGE programs, which carry 6 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 principle (Article 5(1)(c)) and general good-practice standard requiring that only the personal data necessary for a stated purpose be collected and retained. Applied to vector stores, this means storing a non-identifying reference rather than raw personal data in the payload field wherever the application's functionality does not strictly require the raw data to be present in that specific system.
The discipline of recording only the data needed for the trail's purpose and no more, keeping secrets and raw sensitive content out of the log entirely, so that the record stays useful for accountability without becoming a store of liabilities.
The privacy-by-design principle of capturing only the sensor data actually required for a robot's stated task, reducing exposure at the earliest and cheapest point in the lifecycle, before any data has been collected.
The practice of collecting and connecting only the data fields you actually need; both a privacy principle and a maintenance benefit, since fewer fields mean less to keep clean and less exposure if data is lost.
The privacy principle of collecting, sharing, and storing only the minimum amount of personal data necessary for a specific purpose. Reduces risk by limiting what can be exposed in a breach.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Online Safety, Privacy, and Data Basics · Digital Foundations, Certified AI Practitioner: Workplace Foundations
- Data Readiness for Small Operations · Digital Foundations, Certified AI Practitioner: Workplace Foundations
- Embeddings are data too: the vector store as a personal-data system · Feeding the Machines, Certified AI Data Governance Professional (CADGP)
- The deletion decision: the terabytes your organization should destroy this quarter, defended · The Money of Data, Certified AI Data Governance Professional (CADGP)
- The agent audit trail: logging actions so you can reconstruct any decision it made · Agents Under Command, Certified AI Governance Professional (CAIGP)
- Privacy when a robot's sensors record real spaces · Responsible Physical AI in Practice, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Data minimization 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.
Builds minimization, purpose limitation, de-identification, consent and retention into an AI system before launch, with tests that prove it.
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.
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.
- Chief Audit Executive, AI audit editionneeds it at depth
- Chief Privacy Officer, AI privacy editionneeds it at depth
- AI Auditorneeds it at depth · 9 open postings this week
- AI Privacy Engineerneeds it at depth · 6 open postings this week
- Privacy Analystneeds working knowledge
- Senior AI Compliance Analystneeds working knowledge
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: ISO/IEC 27701; GDPR Article 25; EU AI Act Articles 11 and 12; ISO/IEC 42001 Clause 7.5.
One interview question about it
How do you build privacy into an AI product from the design stage rather than checking it at the end?
Asked of candidates for privacy by design and privacy engineering. 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.