{"resourceId":"uf-athletics-databank-governance","versions":[{"version":"external-e3d6b5af66ccb72c9ce8372f019ccc4e02b68e82f453f7de4f2faf33bf3ec2b4","resource":{"id":"uf-athletics-databank-governance","title":"UF Athletics Databank: IRB-governed HiPerGator platform claims HIPAA/FERPA/HITRUST for collegiate AI research","organization":"University of Florida AI-Powered Athletics / University Athletic Association","sector":"NCAA Division I athletics data infrastructure","geography":"United States (Florida)","publishedAt":"undated program page (retrieved 2026-09-29; content describes current UF Athletics Databank)","publicationDate":null,"eventDate":null,"sourceName":"UF AI-Powered Athletics","sourceLabel":"University program documentation","sourceUrl":"https://aipoweredathletics.eng.ufl.edu/the-uf-athletics-databank/","evidenceClass":"vendor-claim","outcomeClass":"emerging","topics":["infrastructure","data-security","governance-procurement","operating-model"],"finding":"UF’s AI-Powered Athletics program describes a centralized, IRB-approved Athletics Databank consolidating performance, health, and academic data for research and operations on HiPerGator. The page asserts HIPAA, FERPA, and HITRUST-aligned controls, role-based access after HIPAA/FERPA training, de-identification for external research, and multi-layer approval (IRB, Data Governance Committee, UAA Research Sub-Committee). It cites literature that little sport-science research occurs in real collegiate environments and that NCAA data often remains siloed. Why now: contrasts injury/recruiting AI product claims with a U.S. Power-conference example of governed data foundations—without publishing new outcome metrics on this page.","sledRelevance":"Direct college-athletics infrastructure and privacy-governance evidence for AI readiness—useful counterweight to model-first injury tools.","evidence":"Program statements on HiPerGator controls, review committees, and literature citations (Abt, Claudino, Luczak, Brewer); no primary outcome table on-page.","architectureImplications":"Central longitudinal lake on institutional HPC with analytics/AI workloads—requires identity, de-identification pipelines, and domain separation of performance/health/academic data.","governanceImplications":"Models a three-committee gate before research access; operational decision-making use still needs clear athlete-facing transparency beyond research IRB.","securityPrivacyImplications":"Explicit HIPAA/FERPA/HITRUST claims and training requirements; still depends on correct classification of athletic vs education vs medical records in practice.","caveats":"University program page; undated; no measured injury/performance outcomes; governance claims not independently audited here.","streamIds":["college-athletics"],"roles":{"sales":"ADs and campus research leaders want AI athletics capability without another siloed wearable dump. Discovery should ask which domains are truly integrated, who may use identifiable vs de-identified data, and what operational decisions the databank already supports. Bound value is a governed research/ops data foundation—not automatic injury or recruiting AI. Unsupported claims include HITRUST equaling zero residual FERPA risk, or that infrastructure alone yields wins/health gains.","engineering":"Fit is an HPC-backed longitudinal athletics data platform for ML research and staff analytics. Integration spans GPS/force plates/EMR/academic feeds with strict RBAC and de-identification exports. Prerequisites: IdP groups, data contracts per source system, and committee workflows. Constraints: program page omits schema and latency SLOs. Security should assume mixed FERPA/HIPAA record types and default-deny external identifiable access. Proof of value: time-to-approved research extract; access-review completion rates; one production ops dashboard with audited lineage.","delivery":"Delivery is platform stewardship plus use-case onboarding through governance gates. Dependencies: UAA, IRB, and HiPerGator ops. Ownership: AI-Powered Athletics/data stewards for platform; sport practitioners for operational interpretations. Skills adoption includes HIPAA/FERPA training before access. Governance checkpoints: IRB + two internal committees for research; periodic access recertification. Acceptance criteria: documented lineage for integrated sources; de-identification verified on external shares; athlete request process. Risks include consent fatigue, research-ops purpose creep, and underused silos despite centralization claims."},"roleDetails":{"sales":{"customerProblem":"NCAA departments collect rich athlete data that stays siloed, blocking responsible AI research and ops.","stakeholders":["AD / UAA leadership","Campus IRB / privacy","Sports performance and medicine","Engineering/research faculty"],"discoveryQuestions":["Which source systems are actually landed today?","What decisions use identifiable vs de-identified data?","How are student-athletes informed of secondary research use?"],"valueHypothesis":"A governed HPC databank can unlock collegiate AI research and clearer ops analytics if access and purpose limits stay enforced.","potentialEngagement":"Data-maturity assessment mapping sources, consent, and a first governed ML use case with committee path.","unsupportedClaims":["Infrastructure alone prevents injuries","HITRUST removes all FERPA obligations","Published win/health outcomes on this page"]},"engineering":{"fit":"Institutional athletics data platform on secure HPC for analytics/AI with research governance gates.","architectureIntegration":"Ingest GPS/force/EMR/academic → governed warehouse on HiPerGator → RBAC apps + de-identified research extracts.","prerequisites":["Source-system data contracts","HIPAA/FERPA training pipeline","De-identification tooling"],"constraints":["Undated program docs","No public schema/SLO","Committee latency for research access"],"security":"Encrypt at rest/in transit; strict RBAC; external shares de-identified only; continuous monitoring as claimed.","proofOfValue":["Approved extract turnaround metrics","Access recertification completion","Lineage for one cross-domain dashboard"]},"delivery":{"work":"Operate ingest/stewardship, run committee workflows, and onboard sport use cases without purpose creep.","dependencies":["HiPerGator capacity","UAA Research Sub-Committee","IRB calendars"],"ownership":"Platform stewards own infrastructure; sport units own operational interpretations; IRB owns human-subjects gates.","skillsAdoption":"Mandatory privacy training; analyst enablement on governed tools.","governanceCheckpoints":["IRB","Data Governance Committee","UAA Research Sub-Committee","Periodic access review"],"acceptanceCriteria":["No external identifiable shares","Documented source coverage","Athlete inquiry path defined"],"risks":["Purpose creep from research to high-stakes ops","Consent gaps","Silos persist under central branding"]}},"retrievedAt":"2026-10-01T03:25:40Z","enrichedAt":"2026-10-01T03:26:50Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Internships/research access can build workforce pathways, but training gates and committee reviews may slow smaller collaborators.","procurementImplications":"Prefer platforms that document FERPA/HIPAA mapping and multi-party access review over model demos alone.","operatingModelImplications":"Institutionalizes data governance as a prerequisite to athletics AI rather than bolting privacy on after model selection.","sourceVerification":{"openedUrl":"https://aipoweredathletics.eng.ufl.edu/the-uf-athletics-databank/","referenceExcerpt":"HiPerGator meets HIPAA, FERPA, and HITRUST standards and provides a robust platform for storing and analyzing sensitive research data.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}