Ground Physical AI Policy in Verifiable Evidence
Regulators and policymakers need frameworks that can be evidenced and verified. The AIhood model provides verifiable machine facts and independent verification results to reference in pilots and policy.
Policy for physical AI is only as strong as the evidence behind it. Self-reported compliance is hard to trust and harder to audit at scale.
What the AIhood Test Evaluates
- Whether machine identity and accountability are verifiable
- Whether operation is authorized and attributable
- Whether operational evidence exists and is intact
- Whether evidence is independently verifiable
Common Evidence Gaps
- Self-attested compliance with no verification
- No standard for what evidence must be captured
- Records that cannot be independently checked
- No trusted-time or tamper guarantees
How AEM Helps
AEM inside implements policy-defined evidence capture — turning regulatory requirements into embedded, verifiable evidence rather than paperwork.
What AuthentAI Verifies
- That evidence referenced in review is authentic
- That timestamps and identity bindings are sound
- That verification results are reproducible
Available Reports
- Verification results and frameworks
- Readiness Reports for pilots
- Aggregate evidence summaries
Institutional Outcome
Regulators review frameworks and reference verification results to inform pilots and policy adoption — building oversight on evidence rather than assertion. AIhood provides verifiable evidence and does not act as a regulator or insurer.
Explore Regulatory Assurance
Insurance boundary
AIhood provides verifiable machine evidence and reports. Coverage, pricing, underwriting and claims decisions are determined by licensed insurance partners.
AIhood is not an insurer or a broker. It does not sell, quote, or approve insurance.