A semantic firewall for proactive governance of synthetic tabular data in generative AI pipelines

Abstract

The generation of synthetic tabular data by unconstrained generative models has become a routine component of modern, data-centric machine-learning pipelines. Suéch generators reproduce the statistical structure of real data, yet statistical fidelity does not guarantee logical validity. A generator may emit records that are plausible with respect to every marginal and correlation of the training data while violating the inter-attribute dependencies that make a record meaningful—for instance, an individual simultaneously never-married and a husband. This failure mode, termed here semantic poisoning, is shown to pass undetected through the distributional monitoring on which pipelines ordinarily rely. Three contributions are made. First, a Semantic Integrity Risk Score (SIRS) is proposed: a learned, generator-agnostic measure of the degree to which a record violates the functional and logical dependencies mined from trusted data. Second, a Semantic Firewall applies SIRS at the point of data ingress to route each record to a pass, review, or quarantine decision. Third, an empirical study on real census data across three structurally distinct generators demonstrates that the proposed signal recovers injected semantic violations with an F1 of 0.86–0.95, whereas marginal drift detection remains near zero; that it generalizes to violations of dependencies withheld from any explicit rule set; and that it prevents downstream harm which aggregate-accuracy monitoring cannot perceive, at a bounded screening cost of approximately thirty milliseconds per thousand records. Semantic screening is thereby positioned as a practical, proactive complement to statistical monitoring rather than a replacement for it.