“How do we use AI without putting student data at risk?”
AI is already on campus: in faculty workflows, student habits, and the vendor products you license. The question is whether it is governed. We help institutions set the rules, protect student records, and run pilots that show what actually helps.
What it looks like
Faculty and staff are already using AI tools, often with no policy, no data agreement, and no way to tell what is working.
Use without policy
Staff and faculty use AI tools every day, with no shared guidance on what data may go into them.
AI arriving through updates
Systems you already license add AI features, and nobody has reviewed what student data they touch.
No evidence of what works
Pilots start with enthusiasm and end without any measure of whether they helped students or staff.
If any of that sounds familiar, here is how we take it apart.
How we solve it
An AI use policy, FERPA-aware data governance, and small measured pilots in advising, admissions, and operations.
Map current use
Find out how AI is already used across teaching, advising, and administration, and which systems hold student records.
Set policy and guardrails
An AI use policy written with faculty, IT, and counsel, plain enough that people actually follow it.
Govern the data
Classify student data, review vendor agreements, and set what may and may not reach an AI system.
Run measured pilots
Small pilots in advising, admissions, or operations, each with a success measure agreed before it starts.
Data governance in this work
Student records carry legal duties, and trust is harder to rebuild than to keep.
- FERPA-aware data classification and handling rules for every AI use.
- Vendor AI features reviewed against your existing data agreements.
- Any student-facing AI evaluated for accuracy and bias before students see it.
What you get
- An AI use policy for faculty, staff, and students
- Student data classification and handling rules
- A review checklist for vendor AI features
- Pilot results with a scale-or-stop recommendation
- Training for faculty and staff
Knowing the approach is half of it. Here is how it gets into production.
Phase by phase, with a gate at each one
Each gate is agreed before its phase begins, so nothing moves forward on optimism.
Assess
Survey current AI use and the systems holding student records.
Gate: Current use and risks mappedGovern
Policy drafted with stakeholders; data rules and vendor review in place.
Gate: Policy approved by governance bodiesPilot
One or two measured pilots against pre-agreed success measures.
Gate: Measure met, or pilot stoppedScale
Roll out what worked, with training, and retire what did not.
Gate: Ongoing
How it runs
Works alongside your IT, academic, and legal teams, within the systems of record you already run.
Go deeper
Tell us where AI is already showing up on your campus.
We will help you decide what to encourage, what to stop, and what to measure.