The Alpha Governance Standard evaluates the enterprise environment around AI, not individual models in isolation. The Alpha 12 defines what Alpha measures, AGR expresses governance quality, and GMI expresses how material AI governance is to the company.
How the framework is used
Alpha separates the standard, the rating opinion, and the materiality view so decision-makers can distinguish governance quality from governance exposure.
AGS
Comprises the Alpha 12 and defines what Alpha evaluates across governance structure, controls, disclosure, resilience, stakeholder impact, and corrigibility.
AGR
Expresses governance quality as an independent rating opinion on the Alpha scale.
GMI
Expresses governance materiality by assessing operational dependency, strategic centrality, risk exposure, and stakeholder impact.
Alpha 12
The Alpha 12 defines the twelve dimensions Alpha evaluates. The rating file applies profile-specific analysis for developers, deployers, and hybrid companies without exposing exact scoring mechanics.
D1
Whether AI governance has named board and executive ownership, clear mandates, reporting cadence, and consequences for failure.
Evidence considered
D2
How the organization tests, constrains, monitors, and responds to material AI system failures and unsafe behavior.
Evidence considered
D3
The quality and consistency of AI governance disclosures to boards, investors, regulators, customers, and the public.
Evidence considered
D4
Governance of the data used by AI systems, including consent, provenance, minimization, privacy, and data supply-chain controls.
Evidence considered
D5
The operating system of AI governance: policies, committees, escalation paths, audit mechanisms, and enterprise-risk integration.
Evidence considered
D6
Whether bias and adverse impacts are identified, measured, remediated, and monitored after deployment.
Evidence considered
D7
How the organization governs AI impacts on workers, customers, communities, and environmental footprint.
Evidence considered
D8
Readiness for current and emerging AI governance requirements without reducing the rating to a compliance checklist.
Evidence considered
D9
Controls for AI-specific security, adversarial misuse, model or agent compromise, and operational resilience.
Evidence considered
D10
Governance of AI vendors, model providers, data suppliers, integration partners, and downstream accountability.
Evidence considered
D11
How the organization identifies, measures, discloses, and remediates AI impacts on affected stakeholders, including customers, workers, communities, and other external parties.
Evidence considered
D12
Whether AI systems can be corrected, overridden, constrained, or shut down when governance requires it, with documented human control and board-visible reporting.
Evidence considered
Principles
We do measure
We do not measure