VeriAudit: An Adversarial Neuro-Symbolic Framework for Industrial Data Product Auditing

As the digital economy shifts from raw data exchange to the circulation of Derived Data Products (DDPs), a core question arises: how can providers trade insights without implicitly leaking proprietary assets? While Trusted Execution Environments (TEEs) provide a secure hardware container for computation, they often suffer from a Semantic Gap, certifying code integrity but remaining blind to high-order statistical leakage, which limits the effectiveness of traditional post-hoc sampling against sophisticated inference attacks. To address this, we propose VeriAudit, an adversarial neuro-symbolic infrastructure integrating Continuous Auditing, Full-population Auditing, and AI Auditing within industrial data ecosystems. A "Dynamic Policy Injection" architecture enables "hot-patching" of audit logic inside running TEEs via remote attestation, without service interruption. Its core combines symbolic constraints for deterministic compliance with a lightweight GNN for adaptive pattern recognition, optimized for TEE memory limits. Evaluations on an industrial Energy-Twin dataset show that VeriAudit achieves a 99.0% recall rate against adaptive attacks where traditional rules exhibit limitations. Sustaining 10,000 TPS at $\sim$20ms latency, VeriAudit serves as a practical paradigm for audit-aware data infrastructure in the modern data factor market.