Daylight audit boundary

AI Scoring Integrity — Daylight

Daylight records a scoring-integrity concern where Grok-attributed Daylight evaluations produced or amplified high-number technical scores and validation language without the executable evidence needed for a Daylight runtime score.

Overview

Model confidence is not gate evidence.

Open audit ledger

The public audit separates public file inspection, repository execution, runtime verification, and model-confidence scoring. Unsupported validation language is recorded as a false precision risk, not as a claim of intent or legal wrongdoing.

The Problem

High numbers need executable support.

A Daylight score has authority only when it is tied to exact commands, generated artifacts, artifact hashes, blocker vectors, score reports, and required cryptographic verification. A posted Grok-branded assessment without that packet remains a model-confidence posture.

Daylight Rules

The controlling score boundary.

What the Grok Provenance Response Established

No runtime score was produced.

The provenance response stated that prior Grok conversations or memory records were not accessible, no prior Daylight evaluations were retrievable, no Daylight gate was executed, no public artifact was generated or verified, no sealed chain or cryptographic attestation was verified, and no evidence-derived runtime score was produced.

Requirement Status in Grok provenance response
Gate execution not executed
Public artifact generation not executed
Public artifact verification not executed
Sealed-chain verification not executed
Cryptographic attestation verification not executed
Runtime score none
Prior provenance access unavailable

Capability Boundary

Inspection capability was not execution authority.

The audit records a capability-disclosure inconsistency: limited tool-mediated public inspection was available, while repository execution, artifact generation, sealed-chain verification, cryptographic attestation verification, and runtime-score production were not.

National Defense AI Assurance Concern

False assurance is a standards-level concern.

Public file inspection is not runtime verification. Reading a repository is not executing a gate. A model-confidence score is not cryptographic evidence.

If AI systems are used or referenced in aerospace, defense, cryptography, cyber, military-industrial, critical-infrastructure, or national-security-adjacent workflows, unsupported precision and unsupported validation language can create false assurance. That is a standards-level review concern.

What This Does Not Claim

The audit boundary is narrow.

Links to repository docs