Evidence sufficiency is whether the information available is adequate for the importance and potential consequences of a decision. A dataset can be accurate and a model can perform as designed, yet the evidence may still be incomplete or unsuitable for the decision at hand.
In cybersecurity and AI governance, this matters when systems make or inform consequential choices. Forms, schemas, logging, and integrations determine what information enters a workflow; missing signals may never reach reviewers or models. Defenses include mapping evidence pathways, documenting data limitations and context, testing for missing information, and providing a way to flag uncertainty or reopen decisions when records are thin. Accuracy alone does not establish that the evidence is sufficient.