The Substrate: A Canonical Definition for AI Governance

This technical note proposes a canonical definition of the substrate as the foundational governance layer of an AI system — the layer that determines what a system is allowed to interpret, amplify, or become. It distinguishes the substrate from runtime controls, ontologies, policies, audits, and wrappers, and establishes it as the physics of AI governability. The substrate is defined as a pre-delivery enforcement layer that governs behavioral drift, privilege allocation, interpretive authority, and meaning formation before outputs reach humans or downstream systems. It is not a corrective mechanism. It is a constitutive constraint on what the system is permitted to be. Five audience-specific register definitions are provided — canonical, executive, operator, regulator, and academic — establishing the substrate concept as communicable across governance, compliance, engineering, and policy domains. The substrate is positioned as a foundational primitive for AI governance architecture, with direct relevance to NIST CAISI, agentic AI safety, and sector-specific compliance frameworks.