1. Adopt a Data Governance Strategy
Data governance serves as the cornerstone for responsible, ethical, secure and effective data utilization within AI systems. Systems are only as safe as the data they’re allowed to access. In higher education, that means carefully controlling which systems — student records, faculty data, research sources — AI tools can query.
The principle of least privilege applies here. AI should only access the data necessary for its intended use. For example, a student querying an AI tool should only receive information they’re authorized to see, not faculty schedules or sensitive research data.
When you implement AI your system should be configured to limit information that would not typically be accessible without AI support.
2. Treat AI Like a Human When Considering IAM
Think of AI as a superuser on your network. It can query multiple systems, correlate data and deliver insights faster than any human. But that also means it must be governed like any other administrator.
DISCOVER: Is your data governance AI-ready?
Strong identity and access management ensures that users are who they say they are and only access what they’re entitled to. AI should follow the same rules. Without IAM, institutions risk unauthorized access, data exfiltration and compliance violations.
AI is an automated admin, and it should be under the same constraints as a human administrator.
3. Employ Data Lifecycle Management to Ensure Quality
AI doesn’t know the difference between current and outdated data unless you tell it. Without strong data lifecycle management, AI tools may surface irrelevant or even harmful information, posing a serious threat to your data integrity.
Institutions must enforce policies for sunsetting old data, especially in systems connected to AI. This includes student records, research files and administrative documents. Otherwise, AI may pull up data that should no longer be accessible.
AI is so good at searching that it may expose data you thought was dead and buried. Implementing lifecycle management policies becomes even more important for data protection in AI-enabled environments.
LEARN MORE: Data governance solutions are a strong foundation for AI adoption.
4. Configure AI Tools With Compliance in Mind.
In higher education, regulations including the Family Educational Rights and Privacy Act govern how student data is handled. Misconfigured AI environments can lead to unauthorized access and legal consequences.
Improper configuration also risks exposing institutional intellectual property. For example, allowing personal use of AI tools outside the institutional domain could result in proprietary data being used to train external models.
This poses a legal risk and a reputation risk. That’s why it’s crucial to be aware of how you’re deploying this technology.
