Data Security for Universities in the Age of AI

Universities have always had a difficult data-security challenge.
They manage large volumes of sensitive information across admissions, academics, financial aid, HR, research, athletics, advancement, student services, and other functions. That information is spread across cloud applications, collaboration platforms, databases, email, file shares, and on-premises systems.

The security problem is not simply finding sensitive data. Universities also need to understand whose data it is, who can access it, how long it should be retained, what regulations apply, and whether that access is appropriate. Artificial intelligence makes those questions more urgent, but it does not replace the underlying data-security problem. AI is another reason universities need a strong foundation for discovering, classifying, governing, and protecting sensitive information.

Southeastern University provides a useful example. As SEU expanded its cybersecurity program, its team wanted better visibility across its environment, stronger controls over access and retention, and a foundation that could also support responsible AI adoption.

That challenge is common across higher education.

1. The first problem is visibility

Sensitive university data is rarely stored in one place.

Student records may be in a student information system, financial-aid documents in shared drives, employee records in HR systems, payment data in business applications, and research information in cloud repositories or file shares.

Over time, copies proliferate. Files are downloaded, emailed, shared, moved, and retained long after their original purpose has passed.

For security teams, this creates a basic but difficult question: Where is the sensitive data?

Without a current inventory, it is difficult to prioritize risk, enforce policy, respond to incidents, or prove compliance.

SEU used Lightbeam to build visibility across more than 180 TB of data and 7.6 million files, including approximately 642,000 files containing sensitive information, across 13 monitored data sources.

That kind of visibility is foundational because every other control depends on knowing what exists.

2. Classification needs university-specific context

Finding sensitive data is only the beginning.

Universities manage many categories of information that may contain similar attributes but require very different treatment.

• Academic records
• Student financial aid
• FAFSA information
• Tuition and loan records
• Payment-card information
• Employee data
• Research data
• Health information
• Donor and advancement records
• Institutional financial information

A generic classification such as “financial data” is often not enough.

SEU, for example, needed to distinguish student financial information from university business financial information because the protections and retention requirements can differ. Its deployment included custom classifications for academic records, student financial information, loans, tuition, and FAFSA data.

The more accurately an institution can classify data, the more precisely it can apply security and governance policies.

 

3. Universities need to know whose data it is

One of the most important distinctions in higher education is identity.

A Social Security number by itself tells you that information is sensitive. It does not tell you whether it belongs to a student, faculty member, employee, applicant, or another individual.

That context can change which regulations apply, who should have access, how long the data should be kept, and what action should be taken if it is exposed.

At SEU, Lightbeam created approximately 1.7 million entities, helping associate sensitive information with students, staff, faculty, applicants, and other populations.

That identity context also improves incident response. If a breach affects a specific group of people, the institution can determine who is actually impacted rather than treating the entire university population as potentially exposed.

4. Excessive access is one of the most immediate risks

Sensitive data is often exposed not because someone broke into a system, but because legitimate access became too broad.

Examples include:

• Public or “anyone with the link” sharing
• Large groups with unnecessary access
• Former employees retaining permissions
• Contractors or external users with stale access
• Sensitive files inherited through broad folder permissions

These are common collaboration-platform problems.

SEU found open-access links that data owners were not necessarily aware of.

This is where discovery has to turn into action.

Universities need to understand not only who technically has access, but whether that access is still necessary.

That is the foundation of least-privilege data security.

 

5. Retention and minimization reduce risk

Another major challenge is deciding how long sensitive data should remain in the environment.

Universities have legitimate reasons to retain many types of records. But sensitive information can also persist indefinitely simply because no one owns the deletion decision.

That creates several problems:

• A larger breach surface
• More information to govern
• Higher storage costs
• Increased legal and regulatory exposure
• More data available to users who may no longer need it

SEU is using its discovery and classification work to help develop retention policies around information such as student loans, FAFSA data, and other regulated records.

A mature data-security program should distinguish between data that must be retained and data that can be securely archived or deleted.

Reducing unnecessary sensitive data is one of the simplest ways to reduce risk.

6. Compliance is not one-size-fits-all

Higher education can be subject to multiple overlapping regulatory and security requirements.

FERPA

FERPA governs access to and disclosure of personally identifiable information in student education records. For universities, the practical implication is that student records need to be identified, protected, and shared only in accordance with applicable requirements and institutional policy.

GLBA

Institutions participating in federal student-aid programs can also face requirements under the Gramm-Leach-Bliley Act related to protecting student financial information. That puts additional emphasis on risk assessment, access control, information inventories, safeguards, and service-provider oversight.

PCI DSS

Universities also process card payments across tuition, bookstores, dining, athletics, events, donations, and other services. PCI DSS applies to applicable payment-card environments and reinforces the need to limit access, minimize stored cardholder data, and maintain appropriate security controls.

HIPAA and health-related data

Depending on the institution and the type of healthcare service involved, HIPAA may apply in some university contexts, while other student health records may instead fall under FERPA. The important security lesson is that identifying “health data” alone may not be enough. The institution needs context about the individual, system, and business purpose.

State, international, and institutional requirements

Universities may also need to account for state privacy laws, breach-notification requirements, GDPR, research-related obligations, contractual commitments, and internal records-retention policies.

Rather than building a separate security process for every regulation, institutions need a common data foundation that can apply different policies based on the data, identity, access, and context involved.

7. A lean security team cannot manage this manually

Higher-education security teams are often responsible for broad environments with limited staffing.

Josh Rhoden said SEU quickly learned that manually relying on native controls in systems such as Google Workspace and Microsoft 365 was not scalable.

He also highlighted the importance of context in improving classification accuracy:

That matters because false positives create operational overhead.

The more accurately a security platform can identify the right data, owner, and context, the more practical it becomes to automate actions around access, retention, and governance.

 

8. AI adds a new reason to get the data foundation right

Artificial intelligence should be viewed as an extension of the university’s data-security strategy, not as an entirely separate problem.

SEU actively encourages appropriate AI use.

Students can use AI for research and writing assistance. Faculty and staff can use it to analyze information and automate repetitive work. The goal is to help the university operate more efficiently and improve the student experience.

But AI introduces another access path to sensitive information.

Universities increasingly need to ask:

• What sensitive data can an AI tool reach?
• Is the user authorized to expose that data to the AI system?
• Is the AI service approved?
• Should certain data be blocked or redacted?
• Is an AI agent operating with broader access than it needs?
• Can the institution prove what was allowed, blocked, or remediated?

SEU has not yet deployed Lightbeam’s AI-specific security controls, but its current work creates the foundation for those future decisions.

Josh described the objective this way:

That is an important distinction.

The goal is not to block AI. It is to apply the same security principles universities already need – visibility, classification, identity context, least privilege, and policy enforcement – to AI interactions as well.

9. A practical data-security model for universities

A balanced university data-security strategy should include seven priorities:

1. Discover sensitive data continuously across cloud, SaaS, collaboration, structured, and on-premises environments.
2. Classify it accurately using university-specific categories rather than relying only on generic PII detection.
3. Connect data to identity and business context so the institution knows whose data it is and why it exists.
4. Reduce excessive access by identifying open links, broad permissions, external access, and stale entitlements.
5. Apply retention and minimization policies so regulated records are preserved appropriately and unnecessary sensitive data is removed.
6. Use the same foundation for compliance across FERPA, GLBA, PCI, and other applicable obligations.
7. Extend those controls to AI over time so approved AI use can expand without giving AI unrestricted access to sensitive information.

This keeps AI in the right place: as an important new use case built on top of a broader data-security program.

Southeastern University: From visibility to stronger control

SEU’s experience reflects that progression.

• 180+ TB of data
• 7.6 million files
• 642,000 sensitive files
• 1.7 million entities
• 13 monitored data sources

It is using that foundation to improve classification, reduce open access, develop retention policies, strengthen access governance, and prepare for responsible AI adoption.

Watch the Southeastern University customer story

Southeastern University: Protecting Student Data and Building the Foundation for Secure AI Adoption

Southeastern University Protects Student Data and Privacy, While Building the Foundation for Secure AI Adoption

 

Read the Southeastern University case study

See how SEU is using Lightbeam to discover and classify sensitive data, map it to student and university identities, identify unnecessary access, strengthen retention and governance, and build a foundation for responsible AI adoption.

Read the Southeastern University Case Study

Data security comes first

The rise of AI does not change the fundamentals of university data security.

It makes them more important.

Universities still need to know what sensitive data they have, where it resides, who it belongs to, who can access it, how long it should be retained, and which rules apply.

AI simply adds another important question: Should this AI system be allowed to use it?

Institutions that build the right data-security foundation now will be better positioned not only to reduce risk and improve compliance today, but also to adopt AI more confidently tomorrow.

Learn more about Lightbeam

Lightbeam is an AI Data Security platform that provides AI Guardrails for Enterprise Data, helping organizations control what sensitive data AI can reach, use, and expose. Powered by the patented Lightbeam Data Identity Graph, Lightbeam maps sensitive data to identity, access, business context, policy, and risk so teams can reduce data exposure, enforce least privilege access, automate privacy and governance workflows, and safely adopt AI.