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This research introduces PCC-SQL, a method that deterministically enforces column-level access policies in Text-to-SQL systems by masking logits based on semantic query roles rather than stochastic refusal. By aligning policy constraints directly with grammar productions, the approach achieves zero data leakage while maintaining high answer coverage and minimal computational overhead. This advancement addresses critical security gaps in deploying generative AI models across trust boundaries where precise data governance is required.
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Policy-Conditioned Constrained Decoding for Column-Level Access Control in Text-to-SQL

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arXiv:2607.12341v2 Announce Type: replace Abstract: Text-to-SQL is increasingly deployed across trust boundaries between data providers and users. Such deployment must balance three competing requirements: policy compliance, answer coverage, and bounded cost. Existing approaches typically decide refusal based on which columns a query mentions and enforce it stochastically. Whether a query is compliant, however, depends not only on which columns appear but on how they are used, and stochastic enforcement cannot deterministically rule out violations. We formalize this requirement as a column-use policy over semantic use: output, filter condition, and aggregation argument. We integrate the policy by aligning each role with grammar productions tracked by the decoder. The resulting system, PCC-SQL, applies a per-token logits mask that deterministically eliminates single-query column-use violations on the supported SQL fragment in a single decoding pass. Across three benchmarks and three open-source models, PCC-SQL achieves 0% Leakage Rate and Coverage up to 88.7% on Spider-CU, while staying within +10% tokens of direct prompting. We additionally assess semantic alignment with execution accuracy.

Abstract:Text-to-SQL is increasingly deployed across trust boundaries between data providers and users. Such deployment must balance three competing requirements: policy compliance, answer coverage, and bounded cost. Existing approaches typically decide refusal based on which columns a query mentions and enforce it stochastically. Whether a query is compliant, however, depends not only on which columns appear but on how they are used, and stochastic enforcement cannot deterministically rule out violations. We formalize this requirement as a column-use policy over semantic use: output, filter condition, and aggregation argument. We integrate the policy by aligning each role with grammar productions tracked by the decoder. The resulting system, PCC-SQL, applies a per-token logits mask that deterministically eliminates single-query column-use violations on the supported SQL fragment in a single decoding pass. Across three benchmarks and three open-source models, PCC-SQL achieves 0% Leakage Rate and Coverage up to 88.7% on Spider-CU, while staying within +10% tokens of direct prompting. We additionally assess semantic alignment with execution accuracy.

Computation and Language (cs.CL); Cryptography and Security (cs.CR); Databases (cs.DB)

arXiv:2607.12341 [cs.CL]

(or arXiv:2607.12341v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.12341 Focus to learn more arXiv-issued DOI via DataCite

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