For university leaders, the academic year will arrive against a backdrop of extraordinary urgency for governing artificial intelligence on campus. Engagement with unapproved AI tools threatens to create data flows that bypass institutional security controls and potentially violate federal privacy mandates. State legislatures are accelerating regulatory activity. The White House released its National Policy Framework for Artificial Intelligence in March 2026 and a subsequent executive order in June, addressing AI cybersecurity and national security. These rapid changes in the political and technological headwinds signal a clear mandate: the question is no longer whether AI governance is necessary, but whether your institution’s governance infrastructure is adequate.
Several areas have emerged as key considerations for institutional attention. This brief guide outlines the most salient risks and recommends mitigation strategies.
Key Legal Risk Areas for Universities
Data Privacy and FERPA Compliance
Higher education institutions handle highly sensitive information. Student records are full of material that is subject to the Family Educational Rights and Privacy Act (FERPA), including academic performance data, disciplinary histories, financial aid details, and health information. AI tools for processing such information are firmly in FERPA’s crosshairs. Vendors may qualify as “school officials” with a “legitimate educational interest” under FERPA’s exception, which permits sharing of such records without consent on a limited basis with school officials who require the data to perform professional responsibilities, but only under the following conditions:
- The institution designates the vendor in its FERPA policy
- The vendor performs services the institution would otherwise perform
- The vendor is under the institution’s direct control regarding the use of records
- The vendor does not re-disclose records without authorization
Institutions must also be able to provide access to AI-maintained records upon request, and disclose a list of approved AI tools and how student data is processed, stored, and protected by AI systems in annual FERPA notifications. State-level student data privacy laws are increasingly setting standards for vendor contracts, third-party integrations, and data retention timelines.
Cybersecurity and Shadow AI
The cybersecurity implications of uncontrolled AI adoption are acute. When higher education workers use AI tools not provided by their institutions, they risk allowing sensitive student data to flow through systems that may lack even basic security controls. Faculty members who use AI for grading or assessment may unknowingly violate student privacy protections, and administrators automating processes through third-party tools may unwittingly create pathways for exported data to bypass institutional security. The June 2026 executive order on AI innovation and security, while directed at frontier AI models, signals a broader federal recognition of the need to address cybersecurity risks that accompany AI capabilities.
Intellectual Property
Generative AI solutions that are used to create commercialized content may not vest the institution with the intellectual property protections it presumes, and may in fact invite infringement claims. Some universities have set policy guidance warning that if generative AI is given access to confidential information or trade secrets, the institution may lose its IP rights to that information, and the information may be disclosed to unauthorized third parties. Likewise, unpublished research uploaded to or processed by AI tools risks being incorporated into responses to other users’ queries, potentially impeding future IP protection.
Academic Integrity and Civil Rights
Academic integrity has become one of the most visible and contentious AI-related issues on campus. The recent decision in Newby v. Adelphi University illustrates the legal risks of over-reliance on AI detection tools. In Newby, a New York court found a university’s conduct “arbitrary and capricious” and ordered an academic integrity violation expunged where the university failed to follow its own written procedures and based its decision on a single AI-detection tool, even as other tools contradicted the finding. The lesson for general counsel is that even if an AI detection tool indicates academic dishonesty, institutions must follow their own procedural safeguards; the technology does not abrogate that obligation.
Civil rights and bias concerns are equally pressing. Schools must consider how AI intersects with Title IX responsibilities, especially with the rise of deepfake technology creating new harassment risks. Algorithmic bias in AI tools used for course placement, behavioral flagging, or learning pathway personalization can produce inequitable outcomes across student groups.
Concrete Steps for General Counsel This Summer
Establish or Strengthen AI Governance Frameworks
A governance framework need not be encyclopedic to be effective but should, at a minimum, account for varying uses of AI across the institution while addressing intellectual property protections and regulatory compliance. General counsel should lead or co-lead the drafting process and ensure that policies are clear, enforceable, and integrated with existing institutional compliance structures rather than treated as standalone documents.
Institutions should also consider assembling a working committee to evaluate AI use on campus, whether in the classroom or in administrative operations. This committee should include representatives from general counsel, IT and information security, academic affairs, human resources, student affairs, research compliance, and procurement. It can also work collaboratively through risk management frameworks, determine needed procedures, and establish standards for vendors offering AI solutions.
Conduct an AI Inventory and Risk Assessment
Institutions cannot govern what they do not know exists. General counsel should partner with IT leadership to survey every department about AI tools in use, identify data flows through unofficial channels, and bring any shadow AI into the light where it can be assessed and either approved with appropriate controls or discontinued. Every AI use case should be evaluated against the institution’s existing legal obligations under FERPA, COPPA, Title IX, the ADA, Section 504, employment discrimination statutes, and applicable state laws.
Review and Renegotiate Vendor Contracts
The EdTech market has exploded with AI-enabled products, including adaptive learning platforms, essay scoring systems, proctoring software, student engagement monitors, and early warning tools for assessing student behavior risks. Many are deployed with minimal institutional oversight of their AI data governance implications. When a vendor’s AI system produces biased outcomes or experiences a data breach, for example, the institution remains accountable to students, families, and regulators. General counsel should develop standard contract language addressing AI vendors’ data governance, require vendor attestations before deploying EdTech products that process student data, and consider joining consortium purchasing programs with shared accountability standards. Key contractual provisions should address whether and how vendors use student information to train AI models, data ownership and retention expectations, breach-notification obligations, and indemnification for AI-related harms.
Staff Training and Awareness
Training should help faculty and staff understand what data can be entered into AI tools, how to evaluate AI outputs, and when to escalate concerns. Short, specific guidance often works better than lengthy compliance modules. General counsel offices should coordinate with human resources and IT to ensure that AI-related training is integrated into onboarding and annual compliance programs and that it is tailored to the specific risks associated with different roles.
Conclusion: The Strategic Role of General Counsel
The institutions that navigate the AI transition most successfully will be those that treat governance not as a constraint on innovation but as a precondition for it. General counsels at higher education institutions are uniquely positioned to lead this effort. They sit at the intersection of regulatory compliance, risk management, vendor contracting, employment law, and institutional policy, precisely the domains where AI governance must be built.
