The Modern Transparency Framework (MTF): AI-Sensitive Data, Reconstruction-Risk Scoring, Safe-Release Transformations, and a Five-Stage Pipeline for Government Data Publication in the AI Era

When the NTSB pulled its entire public investigation database offline after a turbine company owner reconstructed protected cockpit voice recordings from published spectrogram images using freely available software, it exposed a structural gap in government data governance that predates AI by decades. The spectrogram inversion technique—possible since the Griffin-Lim Algorithm of 1984—was never restricted because access barriers were mistaken for technical barriers. AI removed the access barrier. The governance framework had no response because it was built on a threat model that no longer exists. This paper proposes the Modern Transparency Framework (MTF): a ten-component architecture for government data publication that introduces the category of AI-Sensitive Data (AISD), a five-stage transparency pipeline, Reconstruction-Risk Scoring (RRS), Safe-Release Transformations (SRT), multi-board governance review, a transparency metadata layer, continuous monitoring, cross-agency standardization, and a public transparency dashboard. The governing principle: transparency is not the release of raw data. Transparency is the release of useful information under modern threat models.