Structural Digital Twin-Driven Conformance Assessment of LegalTech Governance Ontology: Reproducible Proof of Concept

The increasing convergence of enterprise architecture and IT governance frameworks, such as TOGAF 9.2, COBIT 5, and NIST CSF 1.1, with LegalTech regulations including GDPR 2016/679, eIDAS 910/2014, and NIS2 2022/2555, has created a growing need for rigorous and automated governance-validation mechanisms. However, to date, no formal approach exists to assess semantic conformance between a normative ontological model and its operational implementation in microservice-based systems, leaving critical governance gaps difficult to detect in legally sensitive environments. This paper proposes MALTG (Multidimensional Architecture for LegalTech Governance), a configurable and reusable formal framework that combines OWL 2 ontology engineering, a JSON-LD-based SDT (Structural Digital Twin), semantic conformance mapping, a hierarchical coverage function, and a conformance gap metric to support automated governance assessment and prioritised remediation. The framework accepts any organisational architecture as input, enabling application to arbitrary LegalTech case studies by replacing the reference SDT. The proposed framework models nine governance dimensions through an ontology of 59 classes and 15 properties and validates them against a semi-real, public-source SDT composed of 39 microservice components and 54 directed connections. The reference SDT was populated through an ontology-driven data-collectionprocess: a scraping campaign guided by the MALTG ontology (data/MALTG_Ontology.owl) harvested public information from the official portal of Ecuador’s Council of the Judiciary (Consejo de la Judicatura, CJ)—probing its technological-maturity level and digital-governance compliance—which was condensed into a single JSON-LD artefact, enforcing domain rigour and traceability on the search for LegalTech governance evidence. Experimental results demonstrated an overall ontological score of 82.6 and an SDT score of 73.7, with a mean conformance gap of 8.9. Five dimensions achieved full conformance, while the LegalTech dimension presented the largest gap. Graph-theoretic validation further confirmed monotonic improvement throughout the remediation sequence. Overall, the findings suggest that MALTG provides a formally grounded and reproducible approach for automating multi-framework LegalTech governance conformance assessment while maintaining semantic and structural traceability between normative models and operational architectures. As a proof of concept, this validation relies on a configurable, ontology-driven public-source (semi-real) SDT; external validity in real production LegalTech organisations remains untested and is left for future work.