The Information Model at the center represents the information cycle of Reality → Fact → Evaluation → Policy → Authority → Decision → Execution → Outcome. However, establishing this cycle alone is not sufficient to constitute governance. I2EA applies a set of common design principles across the entire structure.

Traceability

It must be possible to trace which Fact, Evaluation, Policy, Authority, and Decision formed the basis of a governance judgment. Governance requires not only preservation of the final result, but also the ability to reconstruct the state that existed when the judgment was established. This principle leads to Decision-State Evidence (DSE).

Fail Closed

When the necessary governance conditions cannot be verified, the system must not default to permitting Execution. If the applicable Policy, Authority, or other required conditions cannot be established, the action does not proceed to Execution.

Least Privilege

AI systems and AI Agents should not be granted execution authority beyond what is necessary to accomplish their defined purpose. What an AI is technically capable of doing must remain separate from what it is institutionally authorized to execute.

Zero Trust

An AI system, model, AI Agent, user, data source, or existing system is not trusted solely because of its identity, role, or connection to the system. The Policy, Authority, Context, and other governance conditions required for Execution must remain verifiable.

Deterministic Enforcement

Even when AI Reasoning and Evaluation are probabilistic, the final authorization or denial of Execution must not remain ambiguous. At the Execution Boundary, Execution is controlled deterministically according to the given governance state and explicitly defined conditions.

Together, these principles transform the Information Model from a simple information cycle into a governance structure that is traceable, privilege-constrained, verifiable, and capable of explicitly controlling Execution.