Data, privacy, and cybersecurity in schools: A 2025 wake-up call

Key points:

• AI can support personalized learning, but only if safety and privacy are priorities
• Enterprise AI is changing education
• Phishing attacks are evolving, but schools can fight back
• For more on shadow AI and privacy, visit eSN’s Digital Learning hub

In 2025, schools are sitting on more data than ever before. Student records, attendance, health information, behavioral logs, and digital footprints generated by edtech tools have turned K-12 institutions into data-rich environments. As artificial intelligence becomes a central part of the learning experience, these data streams are being processed in increasingly complex ways. But with this complexity comes a critical question: Are schools doing enough to protect that data?

The answer, in many cases, is no.

The rise of shadow AI

According to CoSN’s May 2025 State of EdTech District Leadership report, a significant portion of districts, specifically 43 percent, lack formal policies or guidance for AI use. While 80 percent of districts have generative AI initiatives underway, this policy gap is a major concern. At the same time, Common Sense Media’s Teens, Trust and Technology in the Age of AI highlights that many teens have been misled by fake content and struggle to discern truth from misinformation, underscoring the broad adoption and potential risks of generative AI.

This lack of visibility and control has led to the rise of what many experts call “shadow AI”: unapproved apps and browser extensions that process student inputs, store them indefinitely, or reuse them to train commercial models. These tools are often free, widely adopted, and nearly invisible to IT teams. Shadow AI expands the district’s digital footprint in ways that often escape policy enforcement, opening the door to data leakage and compliance violations. CoSN’s 2025 report specifically notes that “free tools that are downloaded in an ad hoc manner put district data at risk.”

Data protection: The first pillar under pressure

The U.S. Department of Education’s AI Toolkit for Schools urges districts to treat student data with the same care as medical or financial records. However, many AI tools used in classrooms today are not inherently FERPA-compliant and do not always disclose where or how student data is stored. Teachers experimenting with AI-generated lesson plans or feedback may unknowingly input student work into platforms that retain or share that data. In the absence of vendor transparency, there is no way to verify how long data is stored, whether it is shared with third parties, or how it might be reused. FERPA requires that if third-party vendors handle student data on behalf of the institution, they must comply with FERPA. This includes ensuring data is not used for unintended purposes or retained for AI training.

Some tools, marketed as “free classroom assistants,” require login credentials tied to student emails or learning platforms. This creates additional risks if authentication mechanisms are not protected or monitored. Even widely-used generative tools may include language in their privacy policies allowing them to use uploaded content for system training or performance optimization.

 

Data processing and the consent gap

Generative AI models are trained on large datasets, and many free tools continue learning from user prompts. If a student pastes an essay or a teacher includes student identifiers in a prompt, that information could enter a commercial model’s training loop. This creates a scenario where data is being processed without explicit consent, potentially in violation of COPPA (Children’s Online Privacy Protection Act) and FERPA. While the FTC’s December 2023 update to the COPPA Rule did not codify school consent provisions, existing guidance still allows schools to consent to technology use on behalf of parents in educational contexts. However, the onus remains on schools to understand and manage these consent implications, especially with the rule’s new amendments becoming effective June 21, 2025, which strengthen protections and require separate parental consent for third-party disclosures for targeted advertising.

Moreover, many educators and students are unaware of what constitutes “personally identifiable information” (PII) in these contexts. A name combined with a school ID number, disability status, or even a writing sample could easily identify a student, especially in small districts. Without proper training, well-intentioned AI use can cross legal lines unknowingly.

Cybersecurity risks multiply

AI tools have also increased the attack surface of K-12 networks. According to ThreatDown’s 2024 State of Ransomware in Education report, ransomware attacks on K-12 schools increased by 92 percent between 2022 and 2023, with 98 total attacks in 2023. This trend is projected to continue as cybercriminals use AI to create more targeted phishing campaigns and detect system vulnerabilities faster. AI-assisted attacks can mimic human language and tone, making them harder to detect. Some attackers now use large language models to craft personalized emails that appear to come from school administrators.

Many schools lack endpoint protection for student devices, and third-party integrations often bypass internal firewalls. Free AI browser extensions may collect keystrokes or enable unauthorized access to browser sessions. The more tools that are introduced without IT oversight, the harder it becomes to isolate and contain incidents when they occur. CoSN’s 2025 report indicates that 60 percent of edtech leaders are “very concerned about AI-enabled cyberattacks,” yet 61 percent still rely on general funds for cybersecurity efforts, not dedicated funding.

Building a responsible framework

To mitigate these risks, school leaders need to:

• Audit tool usage using platforms like Lightspeed Digital Insight to identify AI tools being accessed without approval. Districts should maintain a living inventory of all digital tools. Lightspeed Digital Insight, for example, is vetted by 1EdTech for data privacy.
• Develop and publish AI use policies that clarify acceptable practices, define data handling expectations, and outline consequences for misuse. Policies should distinguish between tools approved for instructional use and those requiring further evaluation.
• Train educators and students to understand how AI tools collect and process data, how to interpret AI outputs critically, and how to avoid inputting sensitive information. AI literacy should be embedded in digital citizenship curricula, with resources available from organizations like Common Sense Media and aiEDU.
• Vet all third-party apps through standards like the 1EdTech TrustEd Apps program. Contracts should specify data deletion timelines and limit secondary data use. The TrustEd Apps program has vetted over 12,000 products, providing a valuable resource for districts.
• Simulate phishing attacks and test breach response protocols regularly. Cybersecurity training should be required for staff, and recovery plans must be reviewed annually.

Trust starts with transparency

In the rush to embrace AI, schools must not lose sight of their responsibility to protect students’ data and privacy. Transparency with parents, clarity for educators, and secure digital infrastructure are not optional. They are the baseline for trust in the age of algorithmic learning.

AI can support personalized learning, but only if we put safety and privacy first. The time to act is now. Districts that move early to build policies, offer training, and coordinate oversight will be better prepared to lead AI adoption with confidence and care.

About the Author:

Rishi Raj Gera is the Chief Solutions Officer at Magic Edtech. Rishi brings over two decades of experience in designing digital learning systems that sit at the intersection of accessibility, personalization, and emerging technology. His work is driven by a consistent focus on building educational systems that adapt to individual learner needs while maintaining ethical boundaries and equity in design. Rishi continues to advocate for learning environments that are as human-aware as they are data-smart, especially in a time when technology is shaping how students engage with knowledge and one another.