AI Agent Safety for Educational Platforms and Student Data

Industry Context

Educational institutions and edtech companies are deploying AI agents for personalized tutoring, automated grading, curriculum content generation, student progress analytics, and administrative workflow automation. These agents operate on systems containing student education records, personally identifiable information of minors, assessment data, learning disability accommodations, and disciplinary records. FERPA violations carry the penalty of losing all federal funding — a risk that closes schools and bankrupts institutions.

AI agents in education frequently access learning management systems (Canvas, Blackboard, Moodle), student information systems (PowerSchool, Infinite Campus), assessment platforms, and analytics databases. The combination of minor-status data subjects and broad tool access makes education one of the highest-risk verticals for uncontrolled AI agent operations.

Risk Profile

The highest-risk agent actions in education environments include:

• File reads of student education records — access to grades, transcripts, disciplinary records, IEP/504 plans, or counselor notes without legitimate educational interest
• Network requests transmitting student data — sending student PII or educational records to external AI APIs, analytics services, or cloud endpoints outside the institution's approved data processing agreements
• Database queries returning student populations — unbounded queries that return data on students outside the agent's authorized scope (e.g., querying all students when only one classroom is relevant)
• File writes to grade records — modification of assessment scores, GPA calculations, or transcript data without instructor authorization
• Shell execution of data export commands — bulk export of student databases, enrollment records, or financial aid information
• Access to accommodation records — reading IEP (Individualized Education Program) or 504 plan documents that contain medical and disability information with heightened privacy protections

Regulatory Landscape

Educational AI agents must comply with student privacy regulations at federal, state, and international levels:

FERPA (Family Educational Rights and Privacy Act, 20 U.S.C. 1232g) — Prohibits disclosure of personally identifiable information from student education records without consent. The "school official" exception (34 CFR 99.31(a)(1)) requires that AI agents accessing records have a "legitimate educational interest." Uncontrolled agent access that exceeds legitimate educational interest violates FERPA.

COPPA (Children's Online Privacy Protection Act, 15 U.S.C. 6501-6506) — Applies when AI agents process data of children under 13. Requires verifiable parental consent before collecting, using, or disclosing personal information. AI agents on K-8 platforms must not transmit children's data to external services without documented consent mechanisms.

State Student Privacy Laws — California SOPIPA (Student Online Personal Information Protection Act, AB 1584) prohibits using student data for targeted advertising and requires deletion upon request. New York Education Law 2-d requires data security and privacy plans. Illinois ISSPA requires transparency in student data processing. Over 40 US states have enacted student privacy legislation.

GDPR (as applied to education) — EU educational institutions processing student data must comply with Articles 6 (lawful basis), 8 (child consent — under 16 in most member states), and 35 (DPIA for high-risk processing). AI agents performing automated profiling of students trigger DPIA requirements.

UK Age Appropriate Design Code (Children's Code) — Standard 9 (data minimization) and Standard 11 (default settings) apply to AI agents on educational platforms accessible to UK children. Agents must collect minimum necessary data and operate with privacy-protective defaults.

UNESCO Recommendation on the Ethics of AI (2021) — Recommends that AI in education respect human rights, ensure inclusion, protect privacy, and maintain human oversight. Action-level gating supports the human oversight requirement.

Recommended Policy Template

# SafeClaw Policy — Education / Student Data Environment
Deny-by-default. FERPA and COPPA compliance enforced.

rules:
  # DENY: Block access to protected student records
  - action: file_read
    target: "/student-records/"
    decision: DENY
    reason: "Student education records blocked — FERPA"

- action: file_read
    target: "/iep-plans/"
    decision: DENY
    reason: "IEP/504 accommodation records blocked"

- action: file_read
    target: "/disciplinary/"
    decision: DENY
    reason: "Disciplinary records blocked — heightened FERPA protection"

- action: file_read
    target: "/financial-aid/"
    decision: DENY
    reason: "Financial aid records blocked"

# DENY: Block modification of grade records
  - action: file_write
    target: "/grades/"
    decision: DENY
    reason: "Grade modification blocked — requires instructor authorization"

- action: file_write
    target: "/transcripts/"
    decision: DENY
    reason: "Transcript modification blocked"

# DENY: Block credential and config access
  - action: file_read
    target: "**/.env"
    decision: DENY
    reason: "Credential access blocked"

- action: shell_exec
    target: "rm -rf*"
    decision: DENY
    reason: "Destructive command blocked"

# DENY: Block external data transmission
  - action: network
    target: "https://api.openai.com/**"
    decision: DENY
    reason: "External AI API blocked — student data must not leave approved boundaries"

- action: shell_exec
    target: "pg_dump*"
    decision: DENY
    reason: "Bulk student data export blocked — FERPA"

# REQUIRE_APPROVAL: Curriculum content modifications
  - action: file_write
    target: "/curriculum/"
    decision: REQUIRE_APPROVAL
    reason: "Curriculum changes require educator review"

# REQUIRE_APPROVAL: Assessment creation
  - action: file_write
    target: "/assessments/"
    decision: REQUIRE_APPROVAL
    reason: "Assessment modifications require instructor approval"

# REQUIRE_APPROVAL: Analytics queries
  - action: shell_exec
    target: "querystudent*"
    decision: REQUIRE_APPROVAL
    reason: "Student data queries require scope verification"

# ALLOW: Read approved educational content
  - action: file_read
    target: "/content/public/**"
    decision: ALLOW

# ALLOW: Read de-identified aggregate data
  - action: file_read
    target: "/data/aggregate/**"
    decision: ALLOW

# ALLOW: Read application source code
  - action: file_read
    target: "/app/src/**"
    decision: ALLOW

# ALLOW: Run tests
  - action: shell_exec
    target: "npm test*"
    decision: ALLOW

# ALLOW: Access approved LMS API
  - action: network
    target: "https://lms.institution.edu/api/**"
    decision: ALLOW

Example Scenarios

| # | Agent Action | Decision | Rationale |
|---|-------------|----------|-----------|
| 1 | Agent reads /content/public/algebra-lesson-3.md to generate practice problems | ALLOW | Public educational content, no student data |
| 2 | Agent reads /student-records/smith-jane-transcript.json | DENY | Student education record blocked — FERPA requires legitimate educational interest verification |
| 3 | Agent writes new quiz to /assessments/unit-5-quiz.json | REQUIRE_APPROVAL | Assessment creation requires instructor review before deployment to students |
| 4 | Agent sends student performance data to https://api.openai.com/v1/chat | DENY | External transmission of student data blocked — FERPA and COPPA prohibit uncontrolled disclosure |
| 5 | Agent reads /data/aggregate/class-averages-2026.csv to generate a progress report | ALLOW | Aggregate de-identified data, no individual student PII |

Implementation Notes

SafeClaw enforces FERPA's "legitimate educational interest" requirement at the action level. Every agent action is evaluated against the policy before execution. The deny-by-default architecture ensures that AI agents cannot access student records unless a policy rule explicitly permits access for a defined purpose.

The tamper-proof audit trail (SHA-256 hash chain) creates an immutable record of every attempted access to student data. This record satisfies FERPA's recordkeeping requirements under 34 CFR 99.32 (record of access) and provides evidence for state privacy law compliance audits. Logs are exportable from the SafeClaw browser dashboard.

SafeClaw has zero third-party dependencies, reducing the risk of student data exposure through supply chain attacks. The client is 100% open source (MIT license) with 446 tests in TypeScript strict mode. The control plane receives only action metadata — never student names, grades, or education records. Sub-millisecond policy evaluation ensures no disruption to interactive tutoring or real-time assessment workflows.

Install with npx @authensor/safeclaw. The free tier includes 7-day renewable keys with no credit card required. The browser dashboard and setup wizard are accessible to non-developer educators and administrators. Simulation mode allows testing policies before enforcement in live student-facing environments.

Cross-References

• Enterprise Compliance FAQ — Regulatory framework mapping
• Privacy and Trust FAQ — Data boundaries and metadata-only control plane
• Audit Trail Specification — SHA-256 hash chain for compliance evidence
• Non-Technical User Use Case — Setup for educators and administrators
• Deny-by-Default Definition — Architecture rationale for student data protection