ZEGA: A Zero-Knowledge Execution Governance Architecture for Verifiable AI Integrity Without Data Disclosure

Abstract

Contemporary AI governance regimes (GDPR, the EU AI Act, NIST AI RMF) operate declaratively: they mandate outcomes but provide no computational mechanism by which compliance can be verified at execution time without exposing the underlying data. This produces a structural verification asymmetry, the cost of proving integrity is borne by the auditor, who must inspect raw data the operator cannot lawfully or commercially disclose. We propose ZEGA (Zero-Knowledge Execution Governance Architecture), a governance layer in which execution logs are committed cryptographically at capture time, anomaly predicates are evaluated inside zero-knowledge circuits, and regulators verify a succinct proof of integrity without observing a single record. We formalize an Integrity Debt metric ID, quantifying accumulated unverified execution mass, and specify an empirical pipeline over Google BigQuery public datasets (GitHub Archive, 2011–present; >8 billion events) that operationalizes ZEGA’s anomaly-filtering and commitment stages at planetary scale. Executed over a 30-epoch window of 112 million real execution events, the pipeline demonstrates that predicate evaluation is tractable within commodity cloud infrastructure at a stable anomaly base rate of 0.0137% (CV = 0.269). A seven-year longitudinal extraction (2020–2026; 25.4 million events) shows execution volume persistently concentrated in the top decile of actors (66.2% mean share, CV = 0.097), establishing that the baseline ZEGA predicates are calibrated against is structural, not seasonal. We further execute a live zero-knowledge instance over a committed one-hour epoch (45,674 actors), proving the anomaly-rate predicate with a real BN128-curve argument that discloses a single verdict bit and survives forgery and tamper tests, establishing ZK verification with proof size O(log N) and verification time independent of N. ZEGA converts governance from attestation to mathematics: the regulator’s question changes from “show us your data” to “show us your proof.”