How GAFAIG verifies human oversight across AI infrastructure
GAFAIG applies a repeatable verification model to evaluate whether human oversight operates across an organization’s AI infrastructure. Evidence is reviewed, findings are recorded, scoring is deterministic, and certification outcomes are published through the registry.
Organization-wide, evidence-based, and auditable
GAFAIG verification is organization-wide, evidence-based, and designed to produce auditable certification outcomes. Internal evidence remains private, while public disclosure is limited to controlled certification outputs through the registry.
GAFAIG applies structured program criteria to evaluate whether human oversight operates across the organization’s AI infrastructure.
Evidence is collected, linked to review criteria, and assessed as part of a controlled verification process. Internal materials are not disclosed publicly.
Scoring is deterministic and auditable, implemented as reproducible Snowflake-native SQL logic with consistent outputs for the same evidence inputs.
Certification outcomes are published through the GAFAIG Registry. Evidence, findings, and internal assessment materials remain private.
Evidence to certification, then registry publication
Organizations provide governance artifacts, oversight records, and operational documentation supporting review.
Submitted evidence is assessed against program criteria. Findings capture review outcomes and remain linked to the underlying evidence.
Deterministic scoring produces certification outcomes which are then recorded and published through the public registry.
Verification records are auditable and reproducible. Given the same evidence inputs, the scoring framework is designed to produce consistent outcomes.
What gives the framework trust value
- Organization-wide scope
- Evidence-linked certification decisions
- Deterministic and auditable scoring
- Private verification layer with controlled public disclosure
- Reproducible outputs supporting independent trust