Evidence-Centric Certification Maintenance for Learning-EnabledSystems Under Continuous Change

Evidence-Centric Certification Maintenance for Learning-EnabledSystems Under Continuous Change | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evidence-Centric Certification Maintenance for Learning-EnabledSystems Under Continuous Change Sergio Lopez-Flores, Antonio Muñoz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8967881/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Security certification struggles with learning-enabled components because system behaviour depends on data and model artefacts and evolves through DevSecOps/MLOps updates. Traditional schemes often certify a fixed target using point-in-time evidence, so the validity of security claims degrades under rapid releases and multi-party supply chains. This paper maps regulatory and standards-driven requirements to concrete, certificationrelevant evidence across IoT, cloud, edge, and mobile deployments, and identifies recurring gaps. Based on this mapping, we introduce an evidence-centric certification maintenance loop: claims are bound to versioned evidence baselines with provenance, requirement support is captured in a traceability graph (requirements, controls, artefacts, tests, results), and a material-change policy triggers delta-bounded evidence refresh and re-testing. Tool support is restricted to auditable decision assistance (structuring, consistency checking, adequacy support, risk triage, drift/anomaly monitoring); adjudication remains human-led. We demonstrate the loop through four vertical use cases and a pilot ESP32-PICO mTLS/OTA instantiation that defines admissible evidence families, change events, and reassessment triggers; the pilot can be extended to learning-enabled systems by adding model/data lineage and shift-related evidence. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8967881","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602510889,"identity":"ae48ad1b-da82-4b28-a564-7507c4304a60","order_by":0,"name":"Sergio Lopez-Flores","email":"","orcid":"","institution":"University of Malaga","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Lopez-Flores","suffix":""},{"id":602510890,"identity":"39b26c69-c2bc-469c-80e1-db9ead176592","order_by":1,"name":"Antonio Muñoz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAkUlEQVRIiWNgGAWjYLCCDySqZ2ZgnEGyFmYekjTwz+4/+Ni2bZs8A3v7A+K0SNw5zGyc23bbsIHnjAGR1txIZpMGamFskMghUoc8SItl2237BvnnRDrMAKSFse12YoMEA5EOM7yRbGzYc+52chtPDpFa5G4kPnzwo+y2bT/7cSIdBgdsJKofBaNgFIyCUYAPAAAIwShtX1cHnQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Malaga","correspondingAuthor":true,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Muñoz","suffix":""}],"badges":[],"createdAt":"2026-02-25 12:40:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8967881/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8967881/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107749770,"identity":"7b8fcd87-b1b9-4524-8789-6fb6e1e555cc","added_by":"auto","created_at":"2026-04-24 16:55:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6483491,"visible":true,"origin":"","legend":"","description":"","filename":"AICERT.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8967881/v1_covered_b60e68f6-d647-41dc-9f07-84ac53795076.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evidence-Centric Certification Maintenance for Learning-EnabledSystems Under Continuous Change","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8967881/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8967881/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSecurity certification struggles with learning-enabled components because system behaviour depends on data and model artefacts and evolves through DevSecOps/MLOps updates. 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