{"resourceId":"ncaa-performance-technology-recommendations-2026","versions":[{"version":"external-e477adfa5911ce37537d3e6ab182e90b8dde128b678ac4410874035574d349c4","resource":{"id":"ncaa-performance-technology-recommendations-2026","title":"NCAA recommends lifecycle controls for performance technology","organization":"NCAA Committee on Competitive Safeguards and Medical Aspects of Sports","sector":"Collegiate athletics","geography":"United States","publishedAt":"March 2026; exact day unknown","publicationDate":null,"eventDate":null,"sourceName":"Performance Technologies Recommendations: Responsible Use in Collegiate Athletics","sourceLabel":"NCAA consensus guidance, five-page PDF","sourceUrl":"https://ncaaorg.s3.amazonaws.com/ssi/performance/SSI_PerformanceTechRecommendations.pdf","evidenceClass":"standards-guidance","outcomeClass":"cautionary","topics":["governance-procurement","data-security","accessibility-workforce","operating-model"],"finding":"NCAA guidance recommends a written institutional plan, education, data management, technology selection and continuous improvement.","sledRelevance":"Direct guidance for collegiate departments evaluating AI built on athlete performance data; it also covers non-AI technologies.","evidence":"The PDF follows the May 2025 summit and bears a March 2026 footer. It calls for explicit data rights and permissible uses, multidisciplinary review, and preservation of independent medical authority. Consensus recommendations are not an effectiveness trial; no treatment sample or baseline applies.","architectureImplications":"Interpretation: represent approved purposes and data access in system design; a model endpoint alone does not implement governance.","governanceImplications":"Interpretation: translate recommendations into accountable local decisions; these recommendations are not presented here as new binding legislation.","securityPrivacyImplications":"Interpretation: audit vendor access and secondary use before athlete data enters inference or training workflows.","caveats":"No exact publication day found. The document is broader than AI and does not validate any vendor, hosting model or injury prediction claim.","streamIds":["college-athletics"],"roles":{"sales":"Interpretation: The customer problem is fragmented accountability when teams acquire analytics independently. Engage athletics leadership, sports medicine, IT, procurement, counsel and athlete representatives. Ask which systems lack an owner, whether existing contracts permit the planned AI use, and who can stop inappropriate processing. A bounded engagement could inventory one team's tools and resolve its highest-priority governance gaps. The value hypothesis is clearer decisions and fewer unowned risks, not guaranteed compliance or savings. Avoid claiming that adopting a checklist proves safety. Position the work as locally tailored implementation support, with institutional counsel determining legal obligations and medical staff retaining clinical decisions.","engineering":"Interpretation: Fit is a control assessment across collection, storage, inference, reporting and deletion. Build a data-flow map and test effective permissions rather than relying on policy text. Prerequisites include a vendor inventory, named data stewards and an approved use-case register. Examine cloud subprocessors, local-device exports and hybrid copies equally; hosting location by itself is insufficient evidence of protection. A proof of value can trace representative records through each boundary and test revocation and deletion behavior. Require an explicit approval before reusing operational records to train a model. Developers should expose audit evidence; autonomous medical or eligibility decisions are outside this proposed scope.","delivery":"Interpretation: Appoint an athletics executive sponsor and operational coordinator, then assign concrete controls to IT, procurement and health staff. Dependencies include contract access, athlete consultation and time for training. Produce a practical operating procedure and rehearse exceptions before rollout. Governance checkpoints should cover initial inventory, pilot authorization, athlete feedback and renewal. Proposed acceptance criteria are an assigned owner and approved purpose for every in-scope data flow, successful access-removal tests, and documented completion of role-appropriate training. Track unresolved exceptions rather than declaring blanket compliance. Adoption should include a confidential route for athlete concerns; weak follow-through can leave an approved document disconnected from actual practice."},"retrievedAt":"2026-09-07T03:01:04Z","enrichedAt":"2026-09-07T03:04:34Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Interpretation: adapt training formats and test comprehension across staff and athletes.","procurementImplications":"Interpretation: tie purchasing gates to demonstrable suitability and institutionally approved data terms.","operatingModelImplications":"Interpretation: require a maintained operating plan, owners and periodic athlete feedback.","sourceVerification":{"openedUrl":"https://ncaaorg.s3.amazonaws.com/ssi/performance/SSI_PerformanceTechRecommendations.pdf","referenceExcerpt":"Performance technologies should be used responsibly.","promptVersion":"sled-research-v3.1","model":null,"basis":"agent-reported inspection"}}}]}