// Security & Compliance

AI Transparency Surface Evidence Gate

Reconcile owner-defined AI transparency scope with labels, notices, technical markings, and release claims.

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What problem does AI Transparency Surface Evidence Gate solve?

AI providers and deployers must connect product labels, human notices, technical markings, content disclosures, scope decisions, and release claims before qualified legal review.

Use it to

Check owner-defined transparency surfaces before release
Find missing human disclosures and inconsistent labels
Block release claims without supplied surface evidence

What it returns

findings.csv with stable codes and evidence sources
review-manifest.csv with every reviewed record
result.json with READY, REVIEW, or BLOCK gate and SHA-256 receipt
report.md with a human-review handoff

A representative input and result

fixture-backed sample

input Set `rule_snapshot` to `EU-AI-ARTICLE-50-2026-07-20`; `scope_decisions[]` records `scope_id`, owner, evidence reference, and required surface types; `surfaces[]` records labels and disclosures; `release_claims[]` links claims to surface IDs.

result A deterministic READY, REVIEW, or BLOCK result with stable finding codes, a complete review manifest, a human-readable report, and a SHA-256 receipt.

Access and approval boundaries

Terminal permission to run the bundled local Python reconciler.
Read access to one user-selected normalized UTF-8 JSON packet.
Write access only to one user-selected local output directory.
No browser, network, credential, account, environment-variable, messaging, payment, deletion, access-change, filing, or publication permission.

Known limitations

A qualified legal reviewer must define scope and decide whether the recorded surfaces satisfy applicable law.
The dated rule snapshot can become stale and requires revalidation.
The package does not classify AI systems, generate notices, publish changes, or certify compliance.

Questions

Does it connect to a live account or provider?

No. It reads one normalized local JSON packet and makes no network request.

Does it make the final decision or external change?

No. It produces evidence findings; the responsible owner makes every decision and action outside the package.

Is the result deterministic?

Yes. Stable finding codes, sorted records, and a SHA-256 receipt make repeated review inspectable.

What happens with malformed input?

Unreadable JSON, missing arrays, duplicate core identifiers, invalid timestamps, and invalid core numbers fail closed.

Does READY prove the underlying evidence is true?

No. READY means no automated finding appeared in the supplied normalized packet; source truth still requires human review.

AI Transparency Surface Evidence Gate keeps proof and approval boundaries visible.

The listing includes the tested package, realistic samples, declared permissions, and known limitations.

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