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Software bill of materials (SBOM)

A structured list of every code library and package a piece of software depends on, used in software supply chain security to trace exposure when a vulnerability is discovered in a shared dependency; the direct conceptual ancestor of the AI bill of materials.

Defined in 2 GAGE programs, which carry 3 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.

Certified AI Data Governance Professional (CADGP)

An itemized, machine-readable inventory of every component and dependency inside a piece of software, required of federal software vendors in the United States under Executive Order 14028 (2021). Functions as a lineage map for software supply chains specifically, and is vulnerable to the identical name-versus-verification gap this topic teaches unless each listed dependency also records how it was pinned and verified, not just what it is called.

The AI Lobbyist: Certified AI Policy Strategist

A documented inventory of a software product's components, recommended as a transparency practice under Executive Order 14028's software supply chain security directive.

Certified AI Data Governance Professional (CADGP)

A structured list of every code library and package a piece of software depends on, used in software supply chain security to trace exposure when a vulnerability is discovered in a shared dependency; the direct conceptual ancestor of the AI bill of materials.

Where it is taught

The exact lessons this term appears in. The first module of every program is free with a free account.

Where it shows up at work

A definition is where Software bill of materials (SBOM) 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.

AI security fundamentalsSecurity and Resilience

Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.

One lesson 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.

Traces where data came from, what transformed it, who owns each hop and where it flows downstream, so a number can be defended.

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.

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.

“ai security”“prompt injection”“data poisoning”“model theft”“llm security”“red team”

Where the frameworks place it: OWASP Top 10 for LLM Applications; Google SAIF; NIST AI RMF MEASURE 2; EU AI Act Articles 11 and 12.

One interview question about it

What are the security failure modes specific to AI systems, and which conventional control covers none of them?

Asked of candidates for ai security fundamentals. 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.

Terms it appears with

Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.