CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems

CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems | 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 Article CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems Aoun E Muhammad, Kin-Choong Yow, Jamel Baili, Yongwon Cho, Yunyoung Nam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7377133/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 As the deployment of Artificial Intelligence (AI) systems in high-stakes sectors — like healthcare, finance, education, justice, and infrastructure has increased – the possibility and impact of failures of these systems have significantly evolved from being a theoretical possibility to practical recurring, systemic risk. This paper introduces CORTEX (Composite Overlay for Risk Tiering and Exposure), a multi-layered risk scoring framework proposed to assess and score AI system vulnerabilities, developed on empirical analysis of over 1,200 incidents documented in the AI Incident Database (AIID), CORTEX categorizes failure modes into 29 technical vulnerability groups. Each vulnerability is scored through a five-tier architecture that combines: (1) utility-adjusted Likelihood × Impact calculations; (2) governance + contextual overlays aligned with regulatory frameworks, such as the EU AI Act, NIST RMF, OECD principles; (3) technical surface scores, covering exposure vectors like drift, traceability, and adversarial risk; (4) environmental and residual modifiers tailored to context of where these systems are being deployed to use; and (5) a final layered assessment via Bayesian risk aggregation and Monte Carlo simulation to model volatility and long-tail risks. The resulting composite score can be operationalized across AI risk registers, model audits, conformity checks, and dynamic governance dashboards. Physical sciences/Engineering Physical sciences/Mathematics and computing 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7377133","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":513546371,"identity":"ef11ba27-7c54-436d-8c8d-afb2ae559df5","order_by":0,"name":"Aoun E Muhammad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3PsQrCMBCA4ROhXU66RlDzCikFp+KzNBQ6ZegDODjpIo6SwZdwcm0J6FIU5yyFrm6CkxSDk1vsJphvCBnuJxcAx/lNveaJ8ftWfJv0GY6ybolHMFYdtgq2SjEiLvywWl1VPgcaLCwJ0RmvWaX5rqpyJY8QSut6WkSMrzWXRCQKPUisP6JaTEnZnrmkN5O0kFBbwkwyXGBhXvELNVhCwmxJqLM0AkwjiQLUYEPCvS2Z6LRsAGdj6Z+aOz5iOrElH5CZg3w/b/h1p3HHcZz/8QJLskMlK8vC2QAAAABJRU5ErkJggg==","orcid":"","institution":"University of Regina","correspondingAuthor":true,"prefix":"","firstName":"Aoun","middleName":"E","lastName":"Muhammad","suffix":""},{"id":513546372,"identity":"a12b20a0-7d79-4696-886b-97713b9fd7c1","order_by":1,"name":"Kin-Choong Yow","email":"","orcid":"","institution":"University of Regina","correspondingAuthor":false,"prefix":"","firstName":"Kin-Choong","middleName":"","lastName":"Yow","suffix":""},{"id":513546373,"identity":"81d98d28-f907-4fe4-89e5-4fdf1db4dd4e","order_by":2,"name":"Jamel Baili","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Jamel","middleName":"","lastName":"Baili","suffix":""},{"id":513546374,"identity":"29b072a6-8931-4e77-ba4a-edb1af1a3cf2","order_by":3,"name":"Yongwon Cho","email":"","orcid":"","institution":"Soonchunhyang University","correspondingAuthor":false,"prefix":"","firstName":"Yongwon","middleName":"","lastName":"Cho","suffix":""},{"id":513546375,"identity":"a5df5e9e-baf0-4609-9121-b4b499e258c3","order_by":4,"name":"Yunyoung Nam","email":"","orcid":"","institution":"Soonchunhyang University","correspondingAuthor":false,"prefix":"","firstName":"Yunyoung","middleName":"","lastName":"Nam","suffix":""}],"badges":[],"createdAt":"2025-08-14 22:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7377133/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7377133/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91723636,"identity":"d7e29bd1-0715-4b41-b728-0f14a509c01d","added_by":"auto","created_at":"2025-09-19 14:38:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1822259,"visible":true,"origin":"","legend":"","description":"","filename":"CORTEX.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7377133/v1_covered_9aa8be2c-f0d2-4917-a702-31bdf0a86793.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems","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-7377133/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7377133/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As the deployment of Artificial Intelligence (AI) systems in high-stakes sectors — like healthcare, finance, education, justice, and infrastructure has increased – the possibility and impact of failures of these systems have significantly evolved from being a theoretical possibility to practical recurring, systemic risk. 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