I appreciate the question, but I need to be upfront--I'm not an IT leader at a university. I'm an attorney and CPA who's spent 40 years helping clients steer complex legal and financial issues, including data privacy concerns in my law practice. That said, I've worked extensively with educational institutions and small businesses on compliance issues that directly parallel what you're addressing. From my legal practice, I've seen the biggest AI risk isn't the technology itself--it's the disclosure gap. Many institutions rush to implement AI tools without understanding FERPA implications or reading vendor agreements. I had a client almost expose student financial records because their "AI-powered" administrative tool wasn't properly vetting where data was being stored or who had access. The compliance audit alone cost them $40,000. The practical advice I give clients: treat AI vendors like you'd treat a power of attorney--because that's essentially what you're doing with student data. Require explicit data handling protocols in writing, demand regular third-party security audits, and build in contractual penalties for breaches. One education client now requires vendors to carry minimum $5M cyber liability insurance specifically covering AI-related breaches. The real issue is most institutions don't have attorneys reviewing these AI contracts before signing. They're treating software agreements like office supply orders when they should be treating them like healthcare directives--because student data deserves that level of protection.
Student creators now use AI tools every day, but many don't know how their work or personal data is being stored or reused. I see this firsthand when art students upload early drafts to online platforms. At Artmajeur, we learned early that AI models can scrape visual work without clear consent. One case involved a student whose portfolio images were being replicated by external tools. That pushed us to build stronger scanning systems, tighter access controls, and clear opt-out rules. For colleges, the same pattern applies: student work, identity data, and metadata can leak through unsecured AI tools. The fix starts with simple steps: limiting what AI products can store, reviewing how external models train on student content, and publishing clear pages on what data we keep. AI doesn't create the biggest risk; unclear data flows do. When students understand where their work goes, trust rises fast.