External source
DIRECTArticle 14 · Human oversight
High-risk AI systems require effective human oversight appropriate to their risk, autonomy, and context of use.
- Status
- In force · consolidated 2026-07-27
- Source type
- Binding regulation
The Alpha Standard · Crosswalks
Alpha determines whether the enterprise can govern it.
The Alpha Standard maps material AI governance obligations, practices, and threats into a governed evidence and rating system. It makes performance comparable without claiming certification, compliance, or framework equivalence.
The judgment layer
A crosswalk is useful only when it preserves applicability, evidence, and judgment. Select a source to inspect the path Alpha exposes.
External source
DIRECTHigh-risk AI systems require effective human oversight appropriate to their risk, autonomy, and context of use.
Alpha requirement
PRIMARY HOMEMaterial AI and agents must have tested, authorized intervention paths that preserve containment, attribution, and safe restart.
Evidence and test
OPERATING EVIDENCEInspect authority, shutdown, credential revocation, downstream containment, state integrity, and authorized restart.
Alpha judgment
HUMAN APPROVEDThe calculated result may be constrained by a critical-pillar ceiling. Missing applicable evidence produces NR, never an inferred pass.
Crosswalk mappings indicate relevance and possible applicability. They do not constitute legal advice, certification, safe harbor, compliance, or framework equivalence. Source records reviewed 2026-08-04.
What Alpha assesses
Each pillar contains four equal-weight subcategories and eight requirements. A strong average cannot erase a severe failure in a critical pillar.
Who is responsible for AI, and can the board hold them accountable?
20%Can AI operate safely and withstand attack, failure, and disruption?
22%Is data protected, and are AI uses, decisions, and claims clear?
15%Are people treated fairly, protected from harm, and able to challenge decisions?
14%Are legal duties, vendors, models, and external dependencies governed?
15%Can the enterprise detect problems, intervene, correct them, and learn?
14%Framework library
The library is organized by source family and status. Every public record carries a source, review date, and applicability boundary.
Role- and risk-based legal obligations mapped to entity-specific applicability.
In force · consolidated 2026-07-27Govern, Map, Measure, and Manage outcomes traced to Alpha requirements and tests.
AI RMF 1.0 under revisionAI management-system clauses mapped to evidence of authority and operating effectiveness.
Published · 2023Trustworthy-AI principles connected to accountability, rights, transparency, and resilience.
Updated · 2024AI adversary tactics, techniques, and mitigations linked to controls and test evidence.
MaintainedApplication and agentic weaknesses mapped to safety, ecosystem, and intervention controls.
Maintained · 2026 materials reviewedAssurance evidence may support Alpha requirements but does not establish AI governance alone.
Supporting evidenceInformation-security evidence is evaluated within scope, freshness, and relevance limits.
Published · 2022; amended 2024Source library last reviewed .
One rating, two evidence bases
Public and verified observations use the same analytical structure but remain separate. Private evidence cannot enter, alter, or be inferred from a public rating.
Public Information
Filings, policies, regulator records, incidents, litigation, standards activity, and attributable reporting available by the cutoff date.
Verified Assessment
Board records, inventories, tests, change logs, vendor registers, incidents, and corrective actions within a signed assessment perimeter.
Boundary of opinion
An Alpha Governance Rating is an independent governance opinion. It is not a legal conclusion, product certificate, audit opinion, or substitute for accountable decision-making.
Alpha Ratings
Review the methodology, evidence boundaries, and framework mappings with Alpha.