Protocol proposed to record per-decision evidence for AI runtime governance
AIREP protocol aims to standardize signed records of AI system governance decisions—such as releasing, blocking, or redacting outputs—using hash-linked, tamper-evident chains for offline verification.
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- A protocol called AIREP is proposed to record governance decisions made by automated AI runtimes in a standardized, signed format.
- Each record captures a single decision (e.g., release, block, defer, redact, escalate) with a closed set of verbs under a stated policy basis.
- Records reference inputs, outputs, and evidence by cryptographic hash, form a SHA-256 hash chain for tamper detection, and include a mechanical neutrality test to exclude vendor/model-specific content.
- A reference implementation and two-language conformance kit are provided; the format is offered for adoption by any AI runtime that records governance decisions.
A new protocol, AIREP, is proposed to standardize how automated AI runtimes record governance decisions such as releasing, blocking, deferring, redacting, or escalating individual outputs. Each decision is captured as a single signed object that can be checked offline by any party, independent of the runtime that produced it.
Each AIREP record includes a closed set of verbs to describe the decision, a stated policy basis, and cryptographic references (by hash) to the input, output, and evidence—rather than embedding the actual values. This design limits exposure of sensitive data while enabling verification.
Records are organized into a SHA-256 hash chain, binding each record to its position in the sequence. This structure makes tampering or omissions detectable through recomputation, supporting integrity and auditability.
Vendor-, model-, and domain-specific content is confined to an optional namespace, and a mechanical neutrality test is applied to ensure the shared format remains free of such content. This separation aims to preserve interoperability and neutrality across implementations.
The protocol includes a reference implementation and a two-language conformance kit to support adoption. The authors also discuss implementation challenges, including alignment of canonical forms across implementations, freshness witnesses, and multi-runtime chains.
The format is offered for adoption by any AI runtime that records governance decisions, positioning AIREP as a potential standard for transparent, verifiable AI runtime governance.
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