Universities are drowning in AI-built tools. Faculty spin up chatbots; students ship dashboards with FERPA data; departments deploy scripts that talk to third-party APIs with no DPA. Nobody knows what's running, what data it touches, or who's responsible when it breaks.

Traditional IT governance doesn't fit: a faculty member's internal grading helper shouldn't require the same review as a student-facing AI that handles HIPAA data. But ignoring it isn't an option either.

AIF is proportional governance. Low-risk tools register and go. High-risk tools get formal review. Everything in between gets exactly the scrutiny its risk profile demands — scored automatically, analyzed by five independent AI models, and documented for compliance.

How it works

Submit a tool → score 7 dimensions → route to a track → run 5-model pipeline → review.

  1. Intake — 21-question form: what the tool does, who uses it, what data it touches, how it authenticates, whether users know it's AI.
  2. Scoring — 7 weighted dimensions (security, accessibility, data sensitivity, blast radius, autonomy, comprehension, maintenance). Each scored 0–3, weighted by artifact type (public site, internal app, AI agent, data pipeline, etc.), producing a risk percentage.
  3. Track routing — <22% auto-registers; 22–42% self-certifies; 42–65% gets IT review; ≥65% becomes a formal project. Seven escalation conditions (HIPAA, FERPA exposure, non-SSO auth, etc.) force the top track regardless of score.
  4. Agent pipeline — five independent AI models (GPT, MiniMax, MiMo, Kimi, GLM) analyze the codebase using the same prompt. Deterministic tools (Semgrep, ESLint, npm audit, Snyk) run in parallel. Anthropic Opus synthesizes with filesystem access for dispute resolution.
  5. Review — Track 1 auto-activates. Track 2 lets builders self-certify. Tracks 3–4 require reviewer approval. All decisions are audit-logged.
  • AIF demo for the entire pipeline
  • intake — 21 questions, live-scored on the right
  • tool registry — tracks, status, assigned agents
  • review in progress — four agents running
  • code review — findings, severity, file tree
  • how your code gets reviewed — the pipeline, drawn

Confidence tiers in output

TierSourceMeaning
Tool-verifiedSemgrep, ESLint, npm auditDeterministic scanner with a known rule match
Confirmed3+ models agreeIndependent convergence — high confidence
Potential1–2 models flaggedNeeds human review

Four agents

  • Code & Security — 10-section security rubric, SAST scanning, stack-specific deep dive.
  • Accessibility — WCAG 2.2 Level AA audit of every component.
  • QA / Bug Detection — logic bugs, error handling, async/concurrency, edge cases.
  • Documentation + HECVAT — auto-generated user guide, admin guide, compliance summary, HECVAT 4.15 (87-question EDUCAUSE template pre-filled from code analysis).

Portability

AIF was built for the University of Montana but designed to port. INSTITUTION_NAME, INSTITUTION_DOMAIN, AUTH_PROVIDER — three env vars and another institution's in. CAS, header-based (Shibboleth), OIDC/SAML stubs. The 21 intake questions, 7 dimensions, and escalation conditions encode general higher-ed AI governance principles, not UM-specific policy.

Stack

  • React 19
  • Vite
  • Node / Express
  • PostgreSQL 16
  • Docker Compose
  • Pluggable SSO (CAS / Shibboleth / OIDC / SAML)
  • Codex CLI
  • OpenRouter
  • Claude Code CLI
  • Semgrep
  • ESLint
  • Snyk
  • Pandoc
  • xlsx

Standards

NIST AI RMF · NIST CSF 2.0 · WCAG 2.2 · OWASP Top 10 · EDUCAUSE HECVAT 4.15 · ITIL 4.