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EU Research Risk Register

This risk register is for research-software governance. It is not an MDR ISO 14971 risk-management file and not a clinical safety case.

ID Risk Impact Current controls Residual risk Next action
R-001 User mistakes research output for treatment advice Could lead to unsafe real-world decisions README/app/report disclaimers, terms, docs, AI prompt restrictions Medium Keep disclaimers in CI and UI; add onboarding warnings to future apps
R-002 Private CGM/pump/genetic data committed to GitHub GDPR/privacy breach Privacy policy, dataset docs, .gitignore, local-first processing Medium Add optional secret/PHI scan workflow before releases
R-003 Local AI hallucinates clinical interpretation Misleading research conclusion Deterministic run quality gate remains source of truth; AI marked advisory Medium Add structured AI output schema and contradiction checks
R-004 Model or simulator physiology is overclaimed Scientific credibility loss Physiology reference, realism tests, limitations docs, source library Medium Keep calibrating against OhioT1DM and other public datasets
R-005 Desktop app gains broad OS permissions Security and privacy exposure Tauri capability minimisation checked in CI Low Require security review before shell/fs/http/updater plugins
R-006 Dependency vulnerability in app or SDK Compromise or unsafe outputs Dependabot, pip-audit, SBOM, version caps Medium Add Rust cargo audit when Cargo is available in CI
R-007 Dataset license/provenance missing Legal/research integrity issue Dataset registry license/citation checks Low Extend registry to all downloaded HF/public datasets
R-008 Formula drift between docs and code Paper claims no longer match implementation Formula registry, technical dossier, tests Medium Add formula-to-code traceability table per release
R-009 Unsigned desktop updates or downloads User trust/security warnings Signing docs, no automatic updater enabled by default Medium Add notarized macOS and signed Windows releases
R-010 Hugging Face model repo exposes private training data Privacy breach Model card privacy docs, public manifest only Medium Add dataset fingerprinting and no-raw-data release check
R-011 Hardware demos are mistaken for therapeutic devices Safety misunderstanding Bench-only docs, no patient connection, hardware disclaimers Medium Add physical labels and app warning in hardware modes
R-012 Scientific users treat single simulation as evidence Invalid conclusion Reports, evidence bundles, run history, seed recording Medium Encourage multi-seed studies and confidence intervals by default

Review Cadence

  • Review before public release.
  • Review after adding new data connectors, AI models, hardware control, desktop permissions, or updater logic.
  • Review before any university, clinical, or public demo that uses real health data.