As organisations adopt metadata-driven Statistical Computing Environments (SCEs), the next frontier for CDISC standards is capturing analytical provenance.
While CDISC foundational standards, Define-XML, and the evolving Analysis Results Standard (ARS) provide traceability from data collection to analysis results, they do not describe how outputs are produced — including workflows, code versions, environments, and quality checks.
This presentation introduces a CDISC-aligned provenance layer extending ARS and supporting the CDISC360i vision of end-to-end automation. The model formalizes SCE audit-trail elements such as:
Workflow steps and execution metadata
Environment details and code versions
Reviewer actions and quality checks
AI-assisted analytical components
By linking these elements directly to ARS outputs, the proposed approach enables reproducible evidence generation, automated audit readiness, and improved interoperability across tools and vendors.
This framework expands CDISC’s mission from data traceability to full evidence traceability in an increasingly AI-assisted analytical landscape.
