AI Data Governance in Education: Privacy & Security

Trust is the real currency for AI on campus. If your governance is weak, pilots stall and audits get tense. If your governance is strong, faculty adopt faster and students benefit sooner. 

Use this blueprint to design a lean, defensible framework for AI data governance in education that meets privacy rules, locks down security, and makes models explain themselves.

Anchor your policy in recognized frameworks

Build on widely adopted standards so your reviewers and vendors share one language.

• Privacy law in the Philippines. The Data Privacy Act of 2012 (RA 10173) and its Implementing Rules and Regulations require lawful basis, proportionality, transparency, and security controls. Publish a student-facing notice and complete a DPIA for high-risk uses. (National Privacy Commission)
• Risk management for AI. Use the NIST AI Risk Management Framework as your organizing spine for mapping risks, controls, and evaluation. (NIST)
• Security baseline. Align your Information Security Management System to ISO/IEC 27001:2022 to cover risk, controls, and continuous improvement. (ISO)
• Global guardrails. Keep your ethics section compatible with the OECD AI Principles and UNESCO policy guidance for AI in education. (OECD AI)
• EU AI Act awareness. If you collaborate with EU partners, note that some education uses of AI can fall under “high-risk.” Plan for risk management, data governance, and documentation duties. (EU Artificial Intelligence Act)

A 12-control checklist you can actually enforce

Use this as a sprint plan, not a policy museum. Start by picking one high-impact workflow (e.g., grading or advising) and run through each control with clear owners, deadlines, and artifacts. Every item below tells you what to do, what to produce, and how often to review—store those artifacts in a single “AI Governance Evidence” folder your auditors can open in seconds. 

Begin with higher-risk or higher-scale use cases, link outputs to your DPIA, model cards, and dataset datasheets, then expand the checklist program-wide each term.

1) Data inventory & lawful basis

• Do now: List every data element, source, system, and purpose; map each to a lawful basis under RA 10173 (PH Data Privacy Act).
• Produce: One-page register + student/faculty privacy notice.
• Review: Every term (or when a new use case launches).

2) Data minimization & retention

• Do now: Keep only what each use case needs; set retention per data type and automate deletion.
• Produce: Retention schedule + deletion job tickets.
• Review: Semiannual audit.

3) Role-based access (least privilege)

• Do now: Grant access by role, not by person; right-size privileged roles; disable stale accounts.
• Produce: RBAC matrix + quarterly access review log.
• Review: Quarterly (align to NIST-style account management practices).

4) Encryption & key management

• Do now: Enforce TLS in transit and encryption at rest; separate keys from data; log key actions.
• Produce: Key management SOP + KMS audit trail.
• Review: Quarterly control check (ISO 27001 alignment).

5) Vendor & model contracts

• Do now: Execute DPAs that specify purpose, data location, sub-processors, deletion-on-exit, and breach notice SLAs; require model documentation.
• Produce: DPA + model documentation checklist.
• Review: Each renewal or major feature change.

6) Data localization & cross-border transfer

• Do now: Record storage locations; if data leaves the Philippines, document safeguards and counterpart obligations.
• Produce: Cross-border transfer register.
• Review: At onboarding and annually.

7) Incident response

• Do now: Keep a one-page runbook with roles, RTO/RPO targets, comms steps that satisfy RA 10173; run tabletop drills.
• Produce: IR playbook + post-mortem template.
• Review: Twice a year (plus after any incident).

8) Audit logging & monitoring

• Do now: Log auth, access, admin changes, exports, and any model outputs used for advising/grading; make logs immutable.
• Produce: Logging scope + retention policy (≥ one term + one audit cycle).
• Review: Monthly spot checks; formal termly review.

9) Dataset governance

• Do now: Track provenance, consent constraints, sensitive attributes; attach a short “datasheet” to each training/tuning dataset.
• Produce: Dataset catalog with datasheets.
• Review: Before (re)training and annually.

10) Model transparency & evaluation

• Do now: Publish model cards (intended use, limits, subgroup performance); schedule re-evaluation to catch drift.
• Produce: Model card + evaluation report per model.
• Review: Each term or on major data/model change.

11) Human-in-the-loop for consequential decisions

• Do now: Require human review for grading, placement, progression, or financial decisions; provide a “why this result” explanation path.
• Produce: Decision matrix showing where humans approve/override.
• Review: Termly QA check with spot audits.

12) Education & transparency

• Do now: Publish plain-language FAQs on how AI is used, safeguards, and rights to access/correction; include links in syllabi and portals.
• Produce: Student/faculty FAQ + comms plan.
• Review: Each term (or when policy changes).

Ownership & cadence (quick reference)

• Privacy Office / DPO: 1–2, 6, 7, 12
• IT / SecOps: 3, 4, 8
• Procurement / Legal: 5
• Data & AI Team / CTL: 9, 10, 11
• All units: Termly reviews, annual policy refresh

Model and dataset documentation you can copy

Create a lightweight template that ships with every model.

• Intended use and limits
• Training and evaluation data with collection dates and consent constraints
• Performance overall and for key subgroups (first-year students, working adults)
• Known failure patterns and red flags to watch for
• Escalation route and roll-back plan

Pair it with a “datasheet” for each dataset to record provenance, quality checks, and sensitive attributes. (arXiv)

Security controls that stop real attacks

• Identity-first security. Enforce MFA for all staff. Rotate credentials for service accounts with strict bounds.
• Network and app hardening. Restrict admin interfaces to campus IPs or zero-trust access.
• Secure development. Run dependency scanning on prompt-handling services and assessment automation.
• Backups with drills. Quarterly restore tests that include AI workflow services, not only the LMS.
  All of the above should fit inside your ISO 27001 risk register and control library. (ISO)

Ethics and misuse: practical guardrails

• In education contexts, prevent manipulative uses. The EU AI Act prohibits certain practices and treats some education uses as high-risk. Reflect this in your Acceptable Use Policy. (EU Artificial Intelligence Act)
• Content risks. Acknowledge hallucinations and misuse. UNESCO’s recent work highlights harms from inaccurate or harmful AI outputs. Train faculty on verification habits. (AP News)

Implementation sprint: six weeks to go live

Week 1. Form the AI Governance Working Group. Approve scope, glossary, and principles.
Week 2. Complete data inventory and draft DPIA triggers.
Week 3. Publish access matrix and logging plan. Configure SSO and role reviews.
Week 4. Stand up model and dataset documentation templates. Pilot model cards on one workflow.
Week 5. Finalize vendor addendum language and breach playbook.
Week 6. Publish student and faculty FAQs. Run a tabletop incident drill. Launch dashboard with privacy and security KPIs.

Policy KPIs to review each term

• DPIAs completed for high-risk workflows
• Accounts with privileged access reviewed and right-sized
• Percent of models with up-to-date model cards and evaluation reports
• Time to revoke access for separated staff
• Number of incidents, mean time to contain, and post-mortem actions closed
• Student requests for access or correction resolved within SLA

Bringing it all together with PathBuilder

If AI is going to earn trust on campus, governance and learning outcomes have to move in step. PathBuilder gives you both: tight control over data and a clear line of sight from outcomes to evidence.

• Private by design: Role-based access, single sign-on, and comprehensive audit logs keep sensitive data scoped to the right people, with a trail your QA and security teams can verify.
• Evidence-first by default: Assessments, rubrics, and analytics map directly to the outcomes you publish, so attainment is transparent to faculty, students, and reviewers.
• Governed adaptivity: Build outcome-aligned practice with adaptive learning and guide students through personalized learning paths while keeping retention, deletion, and access rules intact.
• Faculty enablement: Share our primer on AI in education to align training with your policy, then use templated model and data documentation to keep courses audit-ready.

When you are ready to operationalize your policy, we can walk your team through a live governance configuration: access matrix, audit logs, model documentation, and an exportable evidence pack. Book a structured demo on About PathBuilder.