Zero Trust AI Validation: An Evidence-Engineering Methodology for AI Data-Use Claims

Private methodology snapshot defining a zero-trust evidence model for independently validating claims about AI data access, provenance, ingestion, and downstream use. It distinguishes observed access, observed consumption signals, linked evidence, and mechanism-supported use, while treating crawler attribution and reproduced content as bounded evidence rather than proof of training. Experimental endpoints, canary values, detection thresholds, and implementation details are excluded. Status: proposed and under empirical validation.