Data Provenance Standards for Trustworthy AI and Data Ecosystems The OASIS Data Provenance Standards Technical Committee advances cross-industry standards for data provenance, lineage, pedigree, and metadata tagging to ensure transparency, accountability, and trust in AI-driven and data-dependent applications. This plan was derived and rendered in StratML format by Claude.ai based upon the public charter, homepage, and founding statements of the OASIS Data Provenance Standards Technical Committee (DPS TC), whose founding sponsors include Cisco, IBM, Intel, Microsoft, and Red Hat. The TC was formally launched on April 8, 2025, building on the Data & Trust Alliance's Data Provenance Standards v1.0.0. ^^ Submitter's Note: It has been lightly edited in the form at https://stratml.us/forms/Claude/Part1.html OASIS Data Provenance Standards Technical Committee DPSTC 2e0ab93c-3743-4840-9c70-b019f2e23bf8 An OASIS Technical Committee co-chaired by Cisco and IBM, with founding sponsors including Intel, Microsoft, and Red Hat, building on the Data & Trust Alliance's Data Provenance Standards v1.0.0 to create de jure cross-industry standards for data transparency, accountability, and trust. Cisco Co-Chair IBM Co-Chair Intel Founding Sponsor Microsoft Founding Sponsor Red Hat Founding Sponsor Lisa Bobbitt TC Co-Chair (Cisco) Bryan Bortnick TC Co-Chair (IBM) A world in which every data asset — across all industries — carries standardized, machine-readable provenance metadata that makes its origin, lineage, rights, and restrictions transparent to all who rely on it, enabling trustworthy AI and data-dependent systems at global scale. 6200585a-7006-4f32-a4e7-28a8a13c6afa To develop and advance actionable cross-industry standards for data provenance — covering tagging and metadata frameworks at the database, table, column, graph, NoSQL, and API levels — that deliver measurable business value, enhance data lifecycle management, and reinforce trust in AI-driven applications. 071dfd52-fefe-43ff-b602-1dedd331175c Transparency Full visibility into where data comes from, how it is created, and whether it can be used legally is the foundation of trustworthy AI and data-dependent systems. Accountability Data provenance standards create auditable records that hold data suppliers, acquirers, and consumers accountable for data quality, rights, and restrictions throughout the data lifecycle. Openness Standards developed through an open, consensus-based process — available to all industries and stakeholders — are more durable, equitable, and widely adopted than proprietary or siloed approaches. Interoperability Common provenance vocabularies and metadata schemas enable seamless data exchange across organizations, industries, and jurisdictions without proprietary lock-in. Provenance Develop and publish cross-industry technical standards for data provenance, lineage, pedigree, and metadata tagging that establish a universal data governance norm applicable across all industries and data structures. da44ca7d-771d-47d5-8753-da1cd219e7c8 1 Tagging Specify consistent metadata tagging frameworks covering database, table, column, graph database, NoSQL database, and API data structures to enable comprehensive data lineage tracking at every level of the data ecosystem. 8e43a0b1-d589-4ad2-a03a-b9cce59a052e 1.1 Rights & Restrictions Define standardized descriptors for data rights, restrictions, sourcing, and permissible use that allow data suppliers to communicate and data acquirers to enforce compliance with privacy, intellectual property, and regulatory obligations. cf4bc245-ff35-430e-9ed9-e2dfe997d1d7 1.2 Vocabulary Establish a shared provenance vocabulary — encompassing provenance, lineage, pedigree, trust, rights, and restrictions — that bridges existing complementary efforts such as C2PA (image provenance) and OpenSSF AIBOM (software supply chain security) without duplicating them. 836862b3-36f4-49d7-a13e-db72161744cc 1.3 AI Trust Reinforce trust in AI-driven systems by ensuring that the data used to train and operate AI models carries transparent, auditable provenance metadata that supports responsible AI development and deployment. e27c3ffb-dd3e-4217-b804-356ddd21e339 2 Governance Enable AI ethics specialists, data governance professionals, and compliance officers to integrate DPS standards into AI model development pipelines, ensuring data used in AI systems is traceable to its origin with documented rights and restrictions. 8e085518-ddac-4714-846f-6a6bc07bba69 2.1 Validation Explore and specify opportunities for automated tools to generate and validate provenance metadata at scale, ensuring standards are adoptable without prohibitive manual overhead and are compatible with modern data pipeline tooling. 448aed78-7426-4747-9a1d-0a8e51527362 2.2 Reasoning Extend provenance concepts from data infrastructure into the AI reasoning layer, addressing questions of what authorized an agent action, what data was accessed under what grants, and whether agent decision chains are traceable after the fact. e1fb94a6-5102-4545-a943-8eb630e0b743 2.3 Adoption & Value Drive industry-wide adoption of data provenance standards by demonstrating clear return on investment for data providers and consumers, reducing the costs of low-quality data, regulatory non-compliance, and trust deficits in AI-driven applications. 041edfba-ddf0-41d6-aee6-a4ad6d7b6814 3 Business Case Document and communicate the measurable business value of provenance standards, including reductions in data cleanup costs, overpayment for low-quality data, regulatory risk exposure, and audit remediation effort. 825f926a-4739-4203-8b1e-fb81934871ce 3.1 Engagement Expand TC membership and participation across diverse industries and geographies, ensuring that standards reflect the full range of data ecosystems and earn legitimacy as de jure global norms through OASIS's open consensus process. bb3bf73c-f814-422c-ac7d-b4c0a45a0908 3.2 Guidance Bridge the gap between standards specification and practical implementation by developing reference implementations, conformance tests, and implementation guides that reduce the barrier to adoption for organizations of all sizes. d890f032-94ed-48da-8179-ae129d4a8787 3.3 2025-04-08 2026-06-08 https://www.oasis-open.org/tc-dps/ Owen Ambur Owen.Ambur@verizon.net