For information used by AI to be treated as “trusted,” it is not sufficient merely to assume that the information is correct. What matters is whether where the information came from, under what Governance it was processed, and what state formed the basis of a Decision can later be verified. Trusted Information Flow connects information, Governance, Decision, and Evidence on the basis of this verifiability.

Information first enters the Digital Environment as Input. Input may include documents, images, audio, video, code, data, and other formats, forming the information environment available to an AI System. However, the mere presence of information in the Digital Environment does not establish its meaning, reliability, conditions of use, or Authority. Fact, Evaluation, Policy, and other Governance Records, together with relevant Governance Context, therefore explicitly structure the attributes, assessments, conditions, and applicable Governance associated with the information.

When an AI System forms a Decision based on this information, the state at that point in time is preserved as Decision-State Evidence (DSE). DSE includes the Evidence necessary to reconstruct the Decision, such as the Prompt / Query, Retrieved Knowledge, Model / Version, Governance Context, Authority, Context, and Decision. This makes it possible to verify not merely “what the AI produced,” but which information and Governance State formed the basis of the Decision.

DSE and related Evidence are also subject to Verification / Audit. Rather than relying solely on explanations provided by the AI System that formed the Decision, the architecture makes the Decision and its Evidence externally verifiable through mechanisms such as Integrity Check, Policy Compliance, Traceability Review, and Anomaly / Deviation Detection. Where Execution occurs, connecting Execution Evidence makes it possible to trace not only the Decision, but also how that Decision was Enforced and what actually occurred.

Trusted Information does not mean information that is unconditionally “correct.” Information may contain errors or uncertainty. What matters is that its Provenance, Governance Context, state at the time of Decision, processing path, Decision, and, where relevant, Execution Outcome are preserved as Evidence, allowing a third party to verify the validity of the information and the Decision.

The foundation of Trust in I2EA is therefore not Assumption, but Verifiability.

Information is trusted because it is verifiable, not because it is assumed.

This principle makes it possible to construct Trust as an externally verifiable Governance Structure, rather than relying solely on the AI System’s internal judgment or self-reporting.