Data Classification for AI

Last updated on 2026-08-27 | Edit this page

Estimated time: 20 minutes

Overview

Questions

  • How do you classify data for AI tool usage?
  • What is the difference between PUBLIC, PROPRIETARY, and RESTRICTED data?
  • Which AI tools are appropriate for each classification?

Objectives

  • Classify research data into the three Pomona tiers
  • Map data classifications to appropriate AI tools
  • Apply the data classification decision tree
  • Document data handling for audit trails

The Three Tiers

Tier 1: PUBLIC

Information that is already public or could be made public without harm.

Examples: Published research papers, public datasets (Kaggle, UCI ML Repository), conference presentations, general knowledge, anonymous aggregated statistics.

AI Tool Policy: May use external AI (ChatGPT, Claude, Gemini, etc.)

Tier 2: PROPRIETARY

Information restricted to specific individuals or organizations.

Examples: Draft research (unpublished), lab notebooks, preliminary results, patent applications, NDA-covered research, proprietary algorithms, grant-funded research with restrictions.

AI Tool Policy: MUST use local AI only (Sagehen HPC GPUs). External AI requires explicit written permission from all stakeholders. Encryption recommended.

Tier 3: RESTRICTED

Highly sensitive information protected by federal law.

Examples: FERPA (student records, grades, IDs), medical/HIPAA data, financial records, export-controlled research (ITAR/EAR), PII (SSN, phone numbers, addresses), biometric data.

AI Tool Policy: LOCAL AI ONLY with mandatory AES-256-GCM encryption. No exceptions.

A table of what is allowed by tier and tool. A hosted assistant is allowed for PUBLIC data, allowed for PROPRIETARY only with approval, and never for RESTRICTED. A local model on Sagehen HPC is allowed for all three tiers. A code assistant working on your own code is allowed for PUBLIC, needs a check with your PI for PROPRIETARY, and is never allowed for RESTRICTED.
The same three tiers, applied to three kinds of tool.
Callout

When in Doubt, Classify as RESTRICTED

If you are unsure about classification, default to RESTRICTED. You can always relax to a lower classification after confirming with your PI, IRB, or the privacy office.

Decision Tree for AI Tool Selection

1. Is the data already publicly available?
   YES -> External AI is acceptable
   NO  -> Continue

2. Does it contain FERPA, HIPAA, financial, or export-controlled data?
   YES -> LOCAL AI ONLY (Sagehen GPU) with encryption
   NO  -> Continue

3. Does it contain NDA, patent, or proprietary methods?
   YES -> LOCAL AI ONLY
   NO  -> Continue

4. Would sharing cause institutional embarrassment?
   YES -> Consider local AI (safer)
   NO  -> External AI is acceptable

Real-World Classification Examples

Student course evaluations: RESTRICTED (FERPA). Use a local model on Sagehen.

CSV with columns [ID, Age, Income, Disease_Status]: Could be RESTRICTED if ID is a real student ID. Remove the ID column to create a PUBLIC version.

Published research papers: PUBLIC. Safe with any AI tool.

Aggregated enrollment statistics (no individuals): PROPRIETARY. Use local AI or obtain written permission.

Safe Data Sharing Pattern

If you must use external AI with potentially sensitive data:

PYTHON 

# STEP 1: Redact sensitive information
original = "Student Alice (ID: 12345) scored 92 on exam"
safe = "Student (ID: XXXXX) scored 92 on exam"

# STEP 2: Use external AI on redacted version only

# STEP 3: Apply results to original data locally
Challenge

Challenge: Classify Your Data

For each scenario, classify the data and recommend AI tools:

  1. A list of 1000 student names and their major
  2. Published research paper about climate change
  3. Your lab’s unpublished results with no identifiers
  4. Code from a public GitHub repository
  5. Export-controlled research paper (marked CONTROLLED)
  1. Student names + major: RESTRICTED (FERPA). Local AI only on Sagehen.
  2. Published climate paper: PUBLIC. External AI safe.
  3. Lab results, anonymized: PROPRIETARY. Local AI preferred; external with written permission.
  4. Public GitHub code: PUBLIC. External AI safe.
  5. Export-controlled: RESTRICTED. Local AI only, with encryption.

The data classification – not the AI tool capability – determines your choice.

Key Points
  • PUBLIC data: safe with external AI tools
  • PROPRIETARY data: local AI only, or external with explicit written permission
  • RESTRICTED data (FERPA, medical, financial, export-controlled): never external AI
  • Use the decision tree to classify before selecting any AI tool
  • Redact personal identifiers before sharing data with any external tool