Evaluating AI Vendors for Student Data Protection

When your school selects an AI-powered EdTech tool, you're not just choosing a product—you're choosing a data partner. That vendor will access some of your most sensitive information: student data.

Not all vendors treat student data with the care it deserves. This guide provides a practical framework for evaluating AI vendors through a data protection lens.

For foundational context on student data protection, see (/insights/student-data-protection-ai-complete-guide).

Executive Summary

• Schools are responsible for vendor compliance with data protection requirements—due diligence is essential
• Key evaluation areas: data collection, processing, storage, security, and retention practices
• Contract terms matter as much as vendor claims—get commitments in writing
• Red flags include vague privacy policies, refusal to sign data processing agreements, and resistance to security audits
• Smaller vendors may lack mature data protection programs; evaluate capability, not just intent
• Regional considerations: verify compliance with PDPA requirements in Singapore, Malaysia, and Thailand
• Document your due diligence process to demonstrate accountability

Why This Matters Now

AI features are proliferating. EdTech vendors are adding AI capabilities rapidly—sometimes without clear communication about new data processing.

Data requirements are expanding. AI tools often need more data than traditional software.

Regulatory scrutiny is increasing. Privacy regulators are paying attention to children's data in educational technology.

Vendor maturity varies widely. Some vendors have robust data protection programs; others are startups learning as they go.

Schools bear accountability. Under PDPA frameworks, schools remain responsible for data protection even when vendors process the data.

For more on vendor security assessment generally, see (/insights/ai-vendor-security-assessment-checklist).

Vendor Evaluation Framework

Category 1: Data Collection and Access

Questions to ask:

• What specific student data will the tool collect or access?
• Why is each data element necessary?
• Can we limit the data shared without losing core functionality?
• Does the tool collect behavioral or biometric data?

Red flags:

• Vague statements about data collection
• Excessive data requirements for simple functionality
• Collecting biometric data without clear necessity

Category 2: Data Processing and AI Use

Questions to ask:

• How does the AI process student data?
• Is student data used to train machine learning models?
• Does the AI create profiles of students?
• Can we opt out of certain AI features?

Red flags:

• Refusal to explain how AI works
• Using student data to train models sold to other schools
• No human oversight in consequential decisions

Category 3: Data Storage and Security

Questions to ask:

• Where is student data stored?
• What security certifications does the vendor hold?
• Has the vendor had security incidents?
• What is the vendor's incident response process?

Red flags:

• No security certifications
• Storing data in jurisdictions with weak privacy laws
• No documented incident response plan

Category 4: Data Sharing and Sub-processors

Questions to ask:

• Does the vendor share student data with third parties?
• Who are the sub-processors?
• Can we approve or veto sub-processor changes?

Red flags:

• Refusal to disclose sub-processors
• Sub-processors in high-risk jurisdictions
• No contractual data protection requirements for sub-processors

Category 5: Data Retention and Deletion

Questions to ask:

• How long is student data retained?
• What happens to data if we terminate the contract?
• Can we request data deletion?

Red flags:

• No defined retention periods
• Retaining data indefinitely
• Data retained after contract termination

Category 6: Contract and Compliance

Questions to ask:

• Will the vendor sign a Data Processing Agreement (DPA)?
• Does the contract allow you to audit the vendor's data practices?
• Is the vendor compliant with PDPA?

Red flags:

• Refusal to sign a DPA
• Complete exclusion of liability for data issues
• Take-it-or-leave-it contract with no negotiation

Vendor Due Diligence Checklist

Before First Contact

• Defined your data protection requirements
• Identified what data you're willing to share
• Understood your regulatory obligations
• Prepared key questions for vendor

Initial Evaluation

• Reviewed vendor privacy policy
• Reviewed vendor security documentation
• Identified data collection scope
• Confirmed AI features and data usage
• Verified security certifications

Deep Dive

• Received complete sub-processor list
• Reviewed sample DPA terms
• Confirmed data storage locations
• Assessed retention and deletion practices
• Checked references from other schools

Contract Negotiation

• Negotiated DPA with appropriate terms
• Secured audit rights
• Confirmed liability allocation
• Specified data deletion requirements
• Established sub-processor approval process

Ongoing Governance

• Scheduled periodic vendor reviews
• Created process for new feature assessment
• Established incident notification procedure
• Documented due diligence for records

Sample DPA Requirements

Your Data Processing Agreement should include:

Scope and Purpose

• Specific data elements being processed
• Permitted purposes (and prohibited uses)
• Duration of processing

Security Requirements

• Minimum security standards
• Required certifications
• Incident notification timeline

Sub-processor Controls

• List of approved sub-processors
• Notification requirement for changes
• Right to object to new sub-processors

Audit Rights

• Right to audit or receive audit reports

Data Subject Rights

• Vendor's obligations to assist with access requests

Termination

• Data return or deletion on termination
• Certification of deletion

Common Failure Modes

Failure 1: Accepting boilerplate terms Prevention: Negotiate. Even small vendors will often modify terms for school customers.

Failure 2: Trusting marketing claims Prevention: Get specifics in writing. Ask for certification evidence.

Failure 3: Ignoring sub-processors Prevention: Require sub-processor disclosure. Assess key sub-processors.

Failure 4: No ongoing monitoring Prevention: Annual vendor reviews. Monitor for feature changes.

Failure 5: Inadequate documentation Prevention: Document everything. You may need to demonstrate accountability later.

Metrics to Track

• Vendors with completed due diligence assessments
• Vendors with signed DPAs
• Vendor security certification status
• Average time to complete vendor assessment
• Vendors requiring remediation

Frequently Asked Questions

Next Steps

Vendor evaluation isn't a one-time event—it's an ongoing relationship. Start with your highest-risk vendors and work through the framework systematically.

Need help establishing vendor evaluation processes?

→ Book an AI Readiness Audit with Pertama Partners. We'll assess your vendor landscape, identify gaps in current agreements, and help you build robust due diligence procedures.

Disclaimer

This article provides general guidance on vendor evaluation for student data protection. It does not constitute legal advice. Consult qualified legal counsel for specific contractual and regulatory guidance.

References

1. Student Data Privacy Consortium. (2024). Model Terms of Service.
2. Future of Privacy Forum. (2023). Student Privacy Principles for AI in Education.
3. PDPC Singapore. (2023). Guide to Data Protection Clauses in ICT Contracts.

Related Articles

• Student Data Protection in the Age of AI: A Complete Guide
• Parental Consent for AI in Schools: Requirements and Templates
• AI Vendor Security Assessment: A Complete Due Diligence Checklist