AI & Student Data Exposure Test.
Find where student data may leave school control through AI tools, EdTech vendors, cloud apps, integrations and staff workflows.
What it checks
From shadow AI to vendor terms.
This assessment reviews the practical pathways where student data can be uploaded, shared, processed, retained, trained on or passed to subprocessors.
- AI tools used by staff or students
- EdTech vendors and classroom apps
- AI-training and model-improvement clauses
- Student data-flow patterns
- Consent, policy and acceptable-use gaps
- LMS, SSO and OAuth integrations
Typical outputs
1
AI / EdTech risk registerTool-by-tool view of risk and recommended use restrictions.
2
Vendor verdictsApproved, conditional, avoid or unknown.
3
Student data-flow mapPlain-English flow of where data moves.
4
Board summaryTop risks, decisions and 30-day actions.
Example questions answered
Make AI use visible before it becomes risky.
Which AI tools are being used?
Identify known and likely AI tools across staff/student use and vendor workflows.
Can student data be uploaded?
Clarify which workflows involve student names, work samples, learning support notes or assessments.
Do vendors train on content?
Review terms for retention, training, subprocessors and school controls.
Give staff clear AI rules backed by evidence.
Turn vendor terms, policies and workflows into a usable school risk view.