Intelligence continuity preserves the relationships among collection sources, analytical evidence, confidence judgments, assumptions, review authority, dissemination restrictions, and later corrections. An assessment should remain connected to what was known when it was issued and to the evidence that subsequently changed its meaning. Similar wording or repeated citations should not be mistaken for independent corroboration. For example, a later report may repeat an earlier claim without introducing a new source. An AI assistant should retain the lineage of the claim and distinguish repetition from additional evidence. When an authorized correction changes an assessment, the original should remain reconstructable while the current judgment is clearly identified. Review questions include whether evidence concerns the same entity, which assumptions remain active, who endorsed the assessment, and whether the intended use falls within its original scope. GovKM's continuity model provides a way to examine these relationships across analytical workflows and institutional memory. This page describes an application area rather than a completed intelligence-system evaluation. Testing can use entirely synthetic, unclassified histories to explore conflicting assessments, withdrawn claims, late evidence, confidence changes, and appropriate abstention when authority or corroboration cannot be established.
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Sector-specific evidence is one view into the broader GovKM continuity model. Continue into the Framework, Research, Articles, OrgAI, or GAIB to understand the relationships behind these applications.