AIRGP: Artificial Intelligence Runtime Governance Protocol — Specification, Version 1.0

The Artificial Intelligence Runtime Governance Protocol (AIRGP) defines a wire-level protocol for runtime AI governance. AIRGP specifies how governance signals — including policies, guardrail verdicts, telemetry data, audit records, cost accounting events, and provenance attestations — flow between AI runtimes, control planes, policy engines, and audit ledgers. AIRGP addresses a critical gap in the current AI governance landscape: while organizational frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001, and regulatory instruments such as the EU AI Act, establish what governance outcomes are required, no existing standard defines how governance decisions are communicated, enforced, and recorded at the wire level during AI system execution. The protocol specifies a JSON-based message format transported over HTTPS (REQUIRED) with optional gRPC support, registered media type application/airgp+json, seven policy primitive types, a tamper-evident audit ledger format using SHA-256 hash chains, Ed25519 message signatures, and three conformance levels (Basic, Enterprise, Sovereign) accommodating deployments ranging from single-application integrations to multi-jurisdictional sovereign cloud environments. AIRGP is a consolidator protocol. It unifies and builds upon fragmented runtime governance efforts including the OpenPort Protocol, Policy Cards, and MI9, providing a single interoperable wire format for the runtime governance layer. Status: Published. Production specification.Steward: ThinkNEO AI Technology Company Limited (Hong Kong, CR No. 2296774).Repository: https://github.com/thinkneo-ai/airgpWeb: https://airgp.space Reference implementation: published separately under Apache License 2.0. Related work by the same author: AI Governance: The Foundation for Organized AI in Production — A Field Guide (DOI 10.5281/zenodo.19655870).