Ai–Blockchain-Enabled Halal Traceability for Cross-Border Certification Harmonization: A Framework for Digital Halal Assurance - Ita Marianingsih; Salim Salim; Sadiq Ibrahim Mijinyawa (LAN TABUR: Jurnal Ekonomi Syariah The Islamic University of KH. Achmad Muzakki Syah Jember, East Java. Jember Jln. Manggar Gebang Poreng 139A Patrang Jember Jawa Timur, 2026)
This study addresses the increasing complexity of cross-border halal trade, where fragmented certification regimes and differing regulatory and fiqh-based approaches raise verification costs and weaken trust. The purpose of this research is to develop and evaluate a digital halal assurance framework that enables Artificial Intelligence (AI)–blockchain halal traceability to support cross-border certification harmonization by making compliance evidence interoperable, verifiable, and auditable. The study uses a qualitative multiple-case design involving regulators, certification bodies and auditors, manufacturers and suppliers, logistics providers, and laboratories. Data were collected through document analysis, semi-structured interviews, and expert review workshops, and analyzed using thematic analysis with cross-case synthesis to derive design requirements. Results show that harmonization relies on institutional arrangements for recognizing evidence; three outputs are pivotal: a shared minimum evidence baseline, a rule-based and updateable equivalence mapping, and a trust registry with accountability and revocation oversight. These elements also shape system architecture toward permissioned consortium governance. Interoperability is the main bottleneck because evidence is dispersed across heterogeneous formats; a minimum data set, selective disclosure, and a hybrid off-chain/on-chain architecture with standardized interfaces and schema versioning are needed to reduce manual reconciliation. Blockchain functions as an evidence engine that anchors audit trails, integrity proofs, and revocation-aware verification via smart contracts. The study concludes that harmonization requires aligning governance, data standards, and evidence mechanisms, and recommends phased implementation and support measures to avoid excluding small and medium enterprises.