Governance Is a Runtime Dependency: An Evidence Map and Operational AI Readiness Matrix for Disaster Management

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Disaster-management AI is commonly governed before deployment through ethics reviews, data-sharing agreements, and standards checklists.This paper argues that design-time governance is necessary but incomplete because decisive failures occur while systems run: a prediction must be overridden, a model rolled back, a decision audited, an incident escalated, or a system kept safe when connectivity degrades.We reframe governance as a runtime dependency and synthesize a purposively screened 80-record set of standards, policy documents, surveys, primary studies, and technical sources.Ten runtime controls are coded through 232 nonexclusive source-control assignments.Coverage averages 23.2 sources per control (median 21.5; range 6 to 43); the five mostcovered controls account for 80.2% of assignments, whereas the five least-covered controls account for 19.8%.A six-family standards crosswalk contains support in 31 of 60 cells (51.7%), with each control represented by two to four instrument families and no family covering all controls.Auditability, coproduction, and human override are prominent, while ownership, rollback, incident reporting, and degraded-mode operation remain comparatively sparse.The contribution is an Operational AI Readiness Matrix with pass/fail questions, evidence requirements, and four maturity levels.An analytical evaluation checks completeness, traceability, maturity discrimination, non-compensation, and scenario coverage, while explicitly recognizing that the matrix has not yet been validated against deployment outcomes.The result is a practical instrument for testing whether disaster-AI systems remain governable during drills and live events, not merely documented before use.