TrustLayer Protocol: A Proposed Trust Infrastructure Framework for Artificial Intelligence

Abstract

Artificial intelligence systems are deployed globally at an unprecedented scale. Yet, no universal mechanism exists to verify that a given AI system is operating within its declared parameters, compliant with applicable regulations, or free from compromise. Trust in AI today is largely assumption-based rather than evidence-based, and this gap is becoming increasingly consequential as AI systems take on greater autonomy in regulated, high-stakes domains. This document proposes the TrustLayer Protocol as one possible architectural framework for addressing this gap. The protocol comprises two complementary components. The Compute Passport Network (CPN) proposes a neutral, global identity and attestation layer for AI compute, models, and training data, establishing cryptographically verifiable records of provenance during the training and development phase. The Universal AI Attestation Protocol (UAAP) proposes an embedded attestation mechanism for deployed AI systems, generating structured, signed claims about runtime operational state, compliance status, and inference provenance. Several UAAP mechanisms, in particular, continuous per-inference behavioral attestation and reasoning verification, remain experimental or require future research, as detailed in Section 6. The TrustLayer Protocol draws on established infrastructure from hardware-based trusted execution environments [1][2], public key infrastructure [3], zero-knowledge proof systems [4][5], and AI governance frameworks, including the EU AI Act [6] and the NIST AI Risk Management Framework [7]. It does not claim to resolve all open problems in AI verification. Rather, it proposes a phased architectural framework within which existing technologies can be composed into a coherent trust infrastructure layer, with clearly identified areas requiring further research and standardization. This specification is published as an open standard by the DefenAware Foundation and is intended as a contribution to ongoing work in AI governance, protocol design, and verifiable AI safety.