Abstract Governance failures in artificial intelligence systems, democratic institutions, and organisational initiatives are commonly treated as domain-specific pathologies addressed through localised controls, policies, or technical safeguards. This paper argues instead that such failures are structurally homologous and can be diagnosed within a stable, domain-independent diagnostic space. Using the Identified Governance Failure Axes as a normative framework, we project documented governance infarctions and actions drawn from multiple domains under invariant axis definitions, fixed ordering, and explicit evidentiary discipline. The AI domain is represented by a corpus of documented AI governance failures, while democratic and organisational domains are represented by external, non-corpus case studies. Axes are marked failed only where the cited material provides semantic support in inference space for absence, violation, or ineffectiveness of the corresponding governance obligation, and are left unmarked where such support is absent. The resulting cross-domain projections exhibit consistent axis activation patterns across otherwise unrelated contexts, showing that the axes characterise governance structure rather than technology-contingent or domain-specific failure modes. The framework therefore functions as a general-purpose diagnostic instrument for analysing, comparing, and informing repair of governance failures in complex socio-technical systems independent of implementation technology or institutional setting. Scope This paper is a normative diagnostic framework paper, not an empirical validation study. It defines a stable diagnostic coordinate space for governance failure and demonstrates its cross-domain applicability under invariant application rules. Claims of statistical prevalence, causal ordering, or remediation efficacy are explicitly out of scope.This is a non-peer reviewed publication anchored for provenance.