{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:41:32Z","timestamp":1781761292816,"version":"3.54.5"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"DARE UKRI"},{"name":"SPRITE+","award":["MC_PC_24038"],"award-info":[{"award-number":["MC_PC_24038"]}]},{"name":"SPRITE+","award":["EP\/W020408\/1"],"award-info":[{"award-number":["EP\/W020408\/1"]}]},{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["INV-057591"],"award-info":[{"award-number":["INV-057591"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UK EPSRC","award":["EP\/S035362\/1"],"award-info":[{"award-number":["EP\/S035362\/1"]}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) systems increasingly rely on complex, multi-layered software supply chains, creating substantial challenges for reproducibility, transparency, and security assurance. Existing software bills of materials inadequately capture AI-specific artefacts such as model lineage, training provenance, and disclosure metadata, limiting verifiable lifecycle governance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study proposes an Artificial Intelligence Bill of Materials (AIBOM) schema that extends the CycloneDX standard through structured schema engineering. The framework integrates cryptographic validation and agent-driven automation to enable machine-verifiable provenance. An autonomous AI pipeline was implemented to conduct continuous environment inspection, vulnerability enrichment, and reproducibility auditing across containerised analytic workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Empirical evaluation demonstrates 98.7% reproducibility fidelity across replicated executions, 96.2% precision in vulnerability matching against reference datasets, and a 63% reduction in manual oversight compared with conventional documentation-based approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The results demonstrate the feasibility of automated provenance assurance and reproducible AI lifecycle validation at scale. The proposed AIBOM framework strengthens software supply chain transparency, enhances provenance integrity, and provides a generalisable methodology for securing AI systems. It further supports alignment with international information security and compliance standards, advancing the scientific foundations of reproducibility engineering in AI-enabled systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcomp.2026.1735919","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T06:44:59Z","timestamp":1768977899000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Operationalising artificial intelligence bills of materials for verifiable AI provenance and lifecycle assurance"],"prefix":"10.3389","volume":"8","author":[{"given":"Petar","family":"Radanliev","sequence":"first","affiliation":[{"name":"Department of Computer Sciences, University of Oxford","place":["Oxford, United Kingdom"]},{"name":"The Alan Turing Institute, British Library","place":["London, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Santos","sequence":"additional","affiliation":[{"name":"Cisco\u2019s Security Research and Operations, Cisco Systems, RTP","place":["San Jose, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carsten","family":"Maple","sequence":"additional","affiliation":[{"name":"The Alan Turing Institute, British Library","place":["London, United Kingdom"]},{"name":"Cyber Security Centre, University of Warwick, WMG","place":["Coventry, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kayvan","family":"Atefi","sequence":"additional","affiliation":[{"name":"Computer Science, School of Digital & Physical Sciences,Faculty of Science and Engineering, University of Hull","place":["Hull, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"ref1","author":"Alrich","year":"2022"},{"key":"ref2","first-page":"141","author":"Beninger","year":"2024"},{"key":"ref3","author":"Biden","year":"2021"},{"key":"ref4","year":"2025"},{"key":"ref5","author":"Can\u00a8ozkan","year":"2024"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-021-00403-w","article-title":"Building resilient medical technology supply chains with a software bill of materials","volume":"4","author":"Carmody","year":"2021","journal-title":"NPJ Digit. 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