1. Adopt a Districtwide Data Governance Strategy
AI systems are only as safe as the data they are allowed to access. That’s why data governance is a critical foundation for responsible, ethical, secure and effective data use within these tools.
In K–12 environments, that means carefully controlling which systems — such as student records, faculty data or student information systems — AI tools can query.
Just as with human users, the principle of least privilege applies here. AI tools should only have access to 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 peer information.
When you implement AI, your system should be configured to limit information that typically would not be accessible without AI support. A best practice is to start small and adjust as needed. Limit AI models’ access to the most basic systems and grant additional privileges only when necessary.
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, districts risk unauthorized access, data exfiltration and compliance violations.
AI is an automated administrator, and it should be under the same constraints as a human admin.
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AI doesn’t know the difference between current and outdated data unless you tell it.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 the integrity of your data
Districts must enforce policies for sunsetting old data, especially in systems connected to AI. This includes student records 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 K–12 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.
This applies to AI use that isn’t authorized by schools too. For example, if teachers load student data into an AI tool outside of the school domain or using their personal computer outside of school, that could result in sensitive data being used to train external models.
This poses a legal risk and a trust and reputation risk. That’s why it’s crucial to be aware of how you’re deploying this technology and how your users are accessing it.
