Legitimate interest assessment (LIA)
The documented, typically three-step analysis used to decide whether legitimate interest is an appropriate basis: identifying the interest, testing whether the processing is genuinely necessary for it, and balancing it against the rights and reasonable expectations of the people affected. The EDPB's Opinion 28/2024 applies this structure to AI development and deployment.
Defined in 2 GAGE programs, which carry 2 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 documented, typically three-step analysis used to decide whether legitimate interest is an appropriate basis: identifying the interest, testing whether the processing is genuinely necessary for it, and balancing it against the rights and reasonable expectations of the people affected. The EDPB's Opinion 28/2024 applies this structure to AI development and deployment.
The written, three-part analysis (purpose test, necessity test, balancing test) an organization documents to justify relying on Article 6(1)(f) for a specific processing activity, built before the processing begins or challenged, not written retroactively to defend it.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- 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)
- Consent archaeology: what this data was collected for versus what you want to do · Data Reality, Certified AI Governance Professional (CAIGP)
Where it shows up at work
A definition is where Legitimate interest assessment (LIA) 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.
Classifies information, applies least privilege, sets retention and acceptable-use rules, and controls what may enter a prompt, a log or an embedding.
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 Privacy Engineerneeds it at depth · 6 open postings this week
- Data Governance Leadneeds it at depth · 2 open postings this week
- Chief Data Officer, data leadership editionneeds working knowledge
- Chief Information Officer, technology leadership editionneeds working knowledge
- Chief Privacy Officer, AI privacy editionneeds working knowledge
- AI Security Architectneeds working knowledge · 8 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: ISO/IEC 27001; GDPR Article 5.
One interview question about it
How do you decide who may access which data for AI work, and how long it is kept?
Asked of candidates for data classification, access and retention. 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 a claim to back up: Classification and retention rules are familiar territory.
- Cybersecurity and IT carries it as work you can show: Access governance and classification are implemented, not described.
- Human resources and people operations carries it as a claim to back up: Access restriction and retention for personnel data are routine.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.