What is this course about?
Most district AI failures are access-control failures, not model failures. This course maps district data across SIS, LMS, assessment, special education, health, and discipline systems, then builds role-based retrieval so an agent inherits the user's permissions rather than the administrator's — plus the governance committee and breach response that has to sit around it.
Who is this course for?
- District data and integration architects
- CTOs and information systems managers
- Privacy officers with technical responsibility
- Student information system administrators
What do I need before starting?
- Familiarity with your SIS, rostering, and identity systems
- Comfort with access control concepts
What will I be able to do afterwards?
- Map district data across every system an agent might reach
- Implement rostering and identity so permissions are inheritable
- Build role-based retrieval that respects the requesting user's scope
- Design retention and deletion schedules that actually execute
- Run breach response for an incident involving an AI system
What does each module cover?
What data does a district actually hold?
45 minA complete inventory across SIS, LMS, assessment, special education, health, and discipline systems.
Objectives
- Inventory every system holding student data
- Classify each by sensitivity tier
- Identify the systems with the weakest access controls
Topics
Activity. Build the district data inventory and tier every system.
How do rostering and identity underpin everything?
50 minOneRoster, SSO, and role definitions as the foundation permissions inherit from.
Objectives
- Map roles from the identity provider through to data access
- Assess rostering data quality and its consequences
- Handle role changes and offboarding
Topics
Activity. Trace one user's role from the identity provider to their SIS data scope.
How does an agent inherit the user's permissions?
60 minRole-based retrieval, and why an agent running with admin scope is the core district AI risk.
Objectives
- Implement retrieval scoped to the requesting user
- Prevent privilege escalation through the agent
- Test cross-boundary access systematically
Topics
Activity. Configure role-based retrieval and run a cross-boundary access test suite.
How do you make retention schedules actually execute?
50 minRetention and deletion that runs rather than existing as a policy document.
Objectives
- Translate retention policy into executable schedules
- Handle deletion across derived data and indexes
- Verify deletion actually occurred
Topics
Activity. Delete one student's record and verify removal across every derived store including embeddings.
Why does de-identification usually fail?
45 minSmall-population re-identification, and why district data resists anonymization.
Objectives
- Explain re-identification risk in small populations
- Assess whether de-identification is achievable for a given use
- Choose alternatives when it is not
Topics
Activity. Attempt re-identification on a de-identified district data set and document what worked.
How do you run a data governance committee?
45 minMembership, cadence, and the authority a committee needs to actually decide.
Objectives
- Define committee membership and decision authority
- Set a cadence that matches the pace of requests
- Design an approval workflow staff will use
Topics
Activity. Draft the committee charter and run a mock approval session on three real requests.
What does breach response look like with an AI system involved?
45 minIncident response when the exposure path runs through retrieval, logs, or model context.
Objectives
- Identify AI-specific exposure paths
- Run containment across model context and logs
- Determine notification obligations and document the analysis
Topics
Activity. Tabletop a retrieval-permissions breach and produce the notification determination.
Building the access-control matrix
60 minThe workshop module: a complete matrix across three agents and four user roles.
Objectives
- Build a complete access-control matrix
- Verify every cell with a test
- Document it for audit
Topics
Activity. Build the matrix and write a passing test for every cell.
What is the capstone project?
District AI data governance package
Produce the complete governance package: data inventory with sensitivity tiers, role-based access-control matrix with passing tests, executable retention schedule with verified deletion, committee charter, and a breach response plan covering AI-specific paths.
Deliverable: A governance package with a tested access-control matrix and verified deletion evidence.
How are learners assessed?
- Cross-boundary access test suite must pass with zero leakage
- Deletion verification across derived stores including embeddings
- Re-identification exercise documented with findings
What ships with the course?
Facilitator guide
Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
Learner workbook
Exercises, checklists, and the templates each module's activity produces.
Hands-on lab environment
A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
Assessment bank
Scenario questions and rubric criteria mapped to each stated learning outcome.
Source bibliography
Every primary regulation and standard cited on this page, linked and dated.
Which AI agents does this course use?
The hands-on modules run against agents already deployable on the ibl.ai platform for k-12.
Where does the course material come from?
Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.
- Student Privacy Policy Office
U.S. Department of Education
Governs the access and retention requirements the matrix implements.
- 1EdTech (IMS Global)
1EdTech Consortium
OneRoster specification underpinning the identity work in Module 2.
- Cybersecurity Framework
NIST
Structures the breach response and control design work.
- Student Privacy Compass
Future of Privacy Forum
District practice guidance on data governance committees.
Delivery notes
Binding guidance for anyone preparing and delivering this course.
- Module 4's embedding deletion is the step districts and vendors both miss. A record deleted from the SIS but still present in a vector index has not been deleted, and the verification exercise must prove it.
- Module 5's re-identification exercise should succeed. Use a small synthetic district where quasi-identifiers genuinely re-identify students — the lesson is that de-identification fails here, and a failed attempt teaches the opposite.
- This is the most technical K-12 course. Do not mix audiences; run K12-1 for policy stakeholders instead.
- The access-control matrix in Module 8 should ship as a reusable template with the test harness attached. Districts will not build the test harness themselves.
- Use a synthetic district throughout. Real rostering data in a workshop environment is exactly the failure this course exists to prevent.
Why run AI training on a platform you own?
You own the course, not a licence to it
Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
Model-agnostic delivery
Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
No per-seat training licences
Usage-based or self-hosted, so cost tracks actual use rather than headcount.
Deploy anywhere
Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.
Frequently asked questions
What does the Data Governance for District AI: From SIS to Agent course cover?
Most district AI failures are access-control failures, not model failures. This course maps district data across SIS, LMS, assessment, special education, health, and discipline systems, then builds role-based retrieval so an agent inherits the user's permissions rather than the administrator's — plus the governance committee and breach response that has to sit around it. It runs 6 hours across 8 modules across 8 modules, at advanced level, and closes with a capstone: District AI data governance package.
Who should take Data Governance for District AI: From SIS to Agent?
It is written for District data and integration architects, CTOs and information systems managers, Privacy officers with technical responsibility, Student information system administrators. Prerequisites: Familiarity with your SIS, rostering, and identity systems; Comfort with access control concepts.
Can we run this course on our own infrastructure?
Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for k-12 teams that cannot send work to a public AI tool.
How do we get access to Data Governance for District AI: From SIS to Agent?
Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.
How much does AI training for k-12 cost on ibl.ai?
There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.