Data classification
The process of categorizing data by sensitivity level (public, internal, confidential, restricted). Determines how data should be stored, shared, and protected. Critical for AI use because pasting confidential data into a consumer AI tool is a classification violation. Regulated industries (HIPAA, GLBA, ITAR) have mandatory classifications that override any generic framework.
Defined in 3 GAGE programs, which carry 5 distinct definitions of it. The wording above is taught in Certified AI Practitioner: Workplace Foundations.
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 process of categorizing data by sensitivity level (public, internal, confidential, restricted). Determines how data should be stored, shared, and protected. Critical for AI use because pasting confidential data into a consumer AI tool is a classification violation. Regulated industries (HIPAA, GLBA, ITAR) have mandatory classifications that override any generic framework.
The step, taken before a model is designed, of labeling each data field by its regulatory category (protected health information, nonpublic personal financial information, personal data, or not regulated), because the classification, not the engineering, determines which vendors, processing locations, retention limits, and access controls are lawful.
The practice of sorting an organization's documents and communications into tiers (this topic uses public, internal, confidential, and restricted) so that security controls can be matched to the actual sensitivity of each category rather than applied uniformly or not at all.
The process of categorizing data by sensitivity level (public, internal, confidential, restricted) to determine appropriate handling, storage, and sharing practices. Essential for knowing what data is safe to enter into AI tools.
An organization's scheme for sorting information by sensitivity, commonly into public, internal, confidential, and regulated/restricted tiers; the foundation under most AI rules, because the data tier determines the permitted tool tier.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Digital Readiness Self-Audit · Digital Foundations, Certified AI Practitioner: Workplace Foundations
- Online Safety, Privacy, and Data Basics · Digital Foundations, Certified AI Practitioner: Workplace Foundations
- Navigating Employer AI Policies · AI in the Workplace and Team Leadership, Certified AI Practitioner: Workplace Foundations
- Protecting Client Intelligence: Information Security for Policy Campaigns · Defensive Operations, The AI Lobbyist: Certified AI Policy Strategist
- The Compliance-First Transformation Playbook · Lab: Regulated Industries, Certified AI Transformation Professional (CATP)
Where it shows up at work
A definition is where Data classification 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.
Applies acceptable-use rules, discloses AI assistance where it matters, and keeps confidential material out of unapproved tools.
One lesson on this page teach it as a primary outcome.
Knows the rules on automated employment decisions, bias audits and notices, and treats hiring AI as the highest-risk use case it is.
One lesson on this page teach it as a primary outcome.
Uses generative AI tools daily, knows what a model can and cannot do, and can say where an output should not be trusted.
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 Literacy Leadneeds it at depth
- AI Adoption and Enablement Leadneeds it at depth
- Ethical AI Specialistneeds working knowledge · 5 open postings this week
- AI Regulatory Counselneeds working knowledge · 14 open postings this week
- Operations Manager, AI-readyneeds it at depth
- AI Program Managerneeds working knowledge · 11 open postings 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 Article 50; OECD AI Principles; NYC Local Law 144; EEOC guidance.
One interview question about it
How do you decide what you may put into an AI tool at work, and how do you tell people when AI helped produce something?
Asked of candidates for responsible and disclosed use of ai at work. 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.
- Human resources and people operations carries it as a claim to back up: Fair-hiring review and disparate-impact awareness are HR discipline.
- Recent graduate in law, policy, business or data carries it as a claim to back up: Most graduates already use the tools daily.
- Teachers, trainers and instructional designers carries it as a claim to back up: Most educators are already using AI in preparation.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.