萝卜AV视频

Schools collect a wide variety of student data, ranging from assignment completion rates, health files, disciplinary records, demographic information, and beyond. When used appropriately, this data can help identify struggling students and personalize learning. But here's the problem: student data is under constant attack. The Cybersecurity and Infrastructure Security Agency (CISA) that 鈥淔or K-12 schools, cyber incidents are so prevalent that, on average, there is more than one incident per school day.鈥� 

AI makes every breach exponentially more dangerous. AI models are growing increasingly sophisticated in their ability to connect the dots across datasets. Algorithms can link seemingly harmless student data to other information sources, generating detailed profiles that may follow students throughout their lives. For children with clean credit histories, this risk compounds over time. 

Imagine: a third-grader's data is exposed today. By the time they鈥檙e ready for college, AI systems may have combined that information with data from several other sources to create synthetic identities, open credit lines, or even file fraudulent tax returns under the child's name. The student who should be choosing a major spends months untangling financial fraud that began when they were eight years old.

The most secure data is the data you don't have. When a breach occurs, bad actors can only steal what information is in the breached system. Data minimization limits your exposure: fewer SSNs stored means fewer SSNs stolen.

Our new resource, "," helps districts systematically review their data practices and implement AI-ready minimization frameworks, starting with quick wins before moving to long-term policy development strategies.

Every day we delay robust data minimization, we increase the risk that students become victims of AI-enabled identity theft. Take action now to limit the data you collect and retain, thereby better protecting your students from AI-fueled identity theft.