GDPR and AI tools
What applies once an international student’s record is in the frame.
Read itWhat we solve
Where a record actually goes
The prompt box is the one everybody worries about. Email, removable drives and the folders that sync a laptop to a personal cloud carry the same material out, and none of those three passes a web proxy.
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A student record leaves in an email
- Outlook
- Gmail
References, letters and grade appeals, going wherever autocomplete put them.
Itzal Mail reads the message and its attachments inside Outlook and Gmail, before the send leaves the client.
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Student detail goes into an AI tool
- ChatGPT
- Claude
- Gemini
- Copilot
A record pasted whole to be summarised: student id, transcript, the student’s name.
Itzal Guard reads the composer, and the file attached to it, on the employee’s own device.
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Exports walk out on a drive
- Drive
- Dropbox
- OneDrive
- USB
A cohort list pulled from the SIS, then uploaded, synced, or copied onto a stick.
Itzal Shield applies one policy to uploads, to USB and to the folders that mirror a laptop.
The record
Whichever of the three stops it, your console is handed the same thing: which identifiers fired, which framework they mapped to, where it was going, and whether that device was covered at the time, with nothing readable anywhere in it.
- Guard
- Shield
Worked example
What a prompt takes with it
The task is ordinary and the tool is the one people already use. Itzal checks the text on the device, in the instant before it is sent.
If it finds something a framework covers it warns or blocks, and your security team gets a record naming the finding types. The message itself never goes anywhere.
Illustration. The data shown is invented.
Why the context rule matters
An education record reaches further than a transcript
Advising notes, disciplinary files, disability accommodations and financial aid are all education records, and they move through the same inbox a transcript does.
Itzal scores an identifier together with the words around it, so the same string is a finding in one sentence and noise in the next. A detector that only matched patterns would have to choose once, for every sentence.
Student J. Okafor, id S2291844, GPA 2.1, on academic probation pending an accommodation review…
A pattern finds the student id. It cannot tell a GPA from any other decimal, and accommodation review is what makes this an education record rather than a note about a number. Invented data.
Scored alongside the identifier
- student id
- transcript
- grade point average
- enrolment record
Seeded by the education template, then yours to edit. How the detection layers work
Compliance
Built for the frameworks you answer to
FERPA and GDPR come on the day you pick the education template, with the terms above seeded. The rest are a licence away, and every one of them reads the same text on the same device.
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FERPA
Education records, which reach further than a transcript and follow a student for life.
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GDPR
Personal data for EU residents, with the lawful-basis question a US record does not carry.
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Your own rules
Internal codenames, your own entity types, and the vocabulary only your organisation uses.
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See it on your own policy
Forty-five minutes with an engineer, against the frameworks you actually answer to.
Book a demo
Also licensed, and off until you ask for them
- State privacy CCPA and CPRA
- PCI DSS Cardholder data anywhere
- GLBA Account and routing numbers
- HIPAA Health data in a claim or a file
A framework here is a detector set and an action, not a certificate. What it finds, and what it does when it finds it, is yours to set per department.
Getting started
You can be covered by this afternoon
One installer, one browser policy per browser family, and the MDM you already run. No appliance, no proxy, and no certificate to distribute.
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Hour 0
Sign in, pick the template
The education template sets your starting policy. Nothing has touched a device yet.
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Hour 1
Push one installer
One signed package per platform, carrying the detection model, so there is nothing else to stage.
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Hour 2
Add the browser policy
One per browser family. Chrome, Edge and Firefox each need their own, and this is the step rollouts get wrong.
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Hour 3
Read coverage, not installs
The console shows whether the composer actually bound on each site. A clean install that never binds looks identical from the installer’s side.
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Same day
Findings start arriving
You can see what is being sent, and to where, before anything warns or blocks.
Those hours are a pilot group, not a fleet. And nothing is warned or blocked until you say so: enforcement waits on a measurement of false positives against your own language, which takes weeks rather than hours. The staged rollout.
Straight answers
The questions a review always asks
They are answered here rather than saved for a call, including the ones where the honest answer is that it depends on your own counsel.
Does FERPA prohibit using AI tools with student data?
FERPA restricts disclosure of personally identifiable information from education records without consent, subject to exceptions such as the school official exception. Whether a given AI vendor qualifies depends on the contract and on your institution's determination.
Does Itzal monitor students?
No. It is deployed on institution-owned staff and faculty devices. Student-owned devices are out of scope by design.
What about research data?
Human subjects data under an IRB protocol is typically higher-sensitivity than FERPA records, and benefits from custom rules (protocol numbers, participant ID formats) rather than generic detection alone.
Does Itzal see the message itself?
No. The check runs in a small program on the device, and the raw text is never transmitted, stored off the device, or written to a log. Your console receives finding types, frameworks and the action taken.
How long does deployment take?
One installer, pushed by your existing MDM. There is no appliance, no proxy and no certificate to distribute, and turning on another product later is a licence change rather than a second rollout.
What does Itzal not see?
Plenty, and we publish it rather than wait to be asked. Coverage is reported per surface and the uncovered ones are named. Surface coverage.
Can people still use AI tools after this is installed?
Yes, and that is the point. Banning them moves the work to a phone you cannot see. Itzal checks what is about to be sent and only intervenes when a rule you set says it should.
What happens when a detection is wrong?
You set the thresholds, and any rule can log rather than block while you tune it. Name detection is measured at 0.959 precision on our corpus, and the figure is published rather than claimed.
Not legal advice. Whether a particular disclosure is reportable is a determination for your own advisers, not for a vendor.
More questions, answeredRead next
The detail behind the two hardest questions
One is what the rules actually say once data is inside a prompt. The other is what your own team is handed afterwards.
Deployment and the legal prerequisites
Staff devices, MDM, and the notice that has to come before any of it.
Read it