DATA-PROOF: Multi-Dimensional Governance-State Assurance for Enterprise Data Transformation and AI Consumption
Abstract: Enterprise data governance distributes controls across identity systems, catalogs, policy engines, lineage services, quality platforms, privacy classifications, and audit stores. Yet a consequential data operation can change governance characteristics after source access has been approved: joins may increase inference risk, reuse may violate purpose, cross-region processing may alter jurisdictional constraints, and AI retrieval may over-collect fields. This paper presents DATA-PROOF, a multi-dimensional governance-state assurance framework that evaluates operations as transitions from a pre-operation state to a predicted post-operation state. The framework integrates purpose compatibility, lineage, quality and freshness, residency, composition risk, minimization, downstream exposure, and policy-version freshness, returning ALLOW, CONSTRAIN, REVIEW, or DENY and recording the decision in a Governance State Transition Certificate (GSTC). A controlled benchmark evaluates 16 governance scenario families with baseline, ablation, certificate-integrity, and latency tests. DATA-PROOF conformed to the configured benchmark policy in 16/16 families, detected 5,000/5,000 certificate mutations, and averaged 54.55 microseconds in-process across 32,000 local executions. The API decision, persistence, and GSTC-verification path was also reproduced successfully under Docker Compose. Results establish prototype feasibility and policy coverage under synthetic semantics, not production effectiveness or legal compliance.
Keywords: Data governance; Derived data; Policy enforcement; AI governance; Runtime assurance
How to Cite?: Naresh Somara, "DATA-PROOF: Multi-Dimensional Governance-State Assurance for Enterprise Data Transformation and AI Consumption", Volume 15 Issue 10, October 2026, International Journal of Science and Research (IJSR), Pages: 241-247, https://www.ijsr.net/getabstract.php?paperid=SR261002073426, DOI: https://dx.doi.org/10.21275/SR261002073426