RuntimeGuard-AI: Scalable Tamper-Evident Accountability for High-Risk AI Systems Under the EU AI Act

The EU AI Act (Regulation 2024/1689) imposes strict transparency and human oversight obligations on high-risk AI systems, specifically under Article 14. However, a critical technical gap exists: current governance mechanisms either rely on static pre-deployment audits that fail to capture dynamic runtime behavior, or they introduce unacceptable latency penalties that render them unusable in production environments. This paper presents RuntimeGuard-AI, an asynchronous governance architecture that separates lightweight inline policy enforcement from batch cryptographic attestation.Our design fundamentally resolves the tension between compliance and performance. By decoupling the critical inference path from the heavy cryptographic machinery required for proofs, we achieve a median latency overhead of just 2.3–4.1%, while enabling cryptographically rigorous, tamper-evident audit trails. Theoretically, we formalize the property of Latency Separationand prove that our architecture satisfies it. Empirically, we implement a complete Zero-Knowledge (ZK) attestation pipeline using the Groth16 proving system on the bls12-381 curve. We measure a witness generation time of 62 msand a total proving time of 1,389 msfor 50,000 constraints on a standard CPU. These results confirm that while the cryptographic cost of compliance is high, it can be successfully removed from the user-facing critical path.To our knowledge, this paper provides the first open-source reference implementation of a compliance architecture designed specifically for Article 14. We contribute: (1) a formalized threat model for AI auditing, (2) the RuntimeGuard protocol for sharded Merkle compliance logging, and (3) a systematic evaluation demonstrating that rigorous regulatory compliance is achievable at scale without compromising the user experience.