Don Cox is a seasoned cybersecurity and IT executive with over 20 years of experience driving digital transformation, risk management, and enterprise security strategy. As the CISO and VP of IT Service Management at American Public Education, Inc. (APEI), Don leads cybersecurity, compliance, and IT operations, ensuring resilience in an evolving threat landscape. A strategic leader with expertise in healthcare, product development, and logistics, he has collaborated with federal agencies on cybercrime investigations. Recognized for visionary leadership, Don is passionate about AI, innovation, and fostering a security-first culture to enable business growth and operational excellence.
The Growing Role of AI in Higher Education
Artificial intelligence (AI) is revolutionizing higher education, bringing significant changes to admissions, research, academic integrity, student support, cybersecurity, and administrative operations. Universities are increasingly relying on AI-driven tools to streamline processes, enhance learning experiences, and improve institutional efficiency. However, AI adoption also raises concerns about data privacy, algorithmic bias, transparency, and regulatory compliance.
To ensure responsible and ethical AI use, higher education institutions must implement a comprehensive AI Governance, Risk, and Compliance (AI GRC) framework that safeguards student data, ensures fairness, and aligns AI deployments with institutional and legal requirements.
Establishing an AI Governance Framework
A well-defined AI governance framework is essential for maintaining integrity, security, and transparency in AI applications. Institutions must create policies that align AI use with academic values while ensuring compliance with FERPA, GDPR, and other regulations.
To oversee AI implementation, universities should establish a dedicated AI governance committee, composed of leaders from IT, cybersecurity, legal, ethics, faculty, and student bodies. This committee should define clear principles guiding AI’s role in admissions, grading, and research, ensuring it promotes fairness, transparency, and accessibility.
For AI models used in decision-making processes, documentation is crucial. Universities must require vendors and internal teams to disclose data sources, training methodologies, and bias mitigation techniques. AI-driven decisions, particularly in areas such as grading and admissions, should be auditable and explainable to maintain trust among students and faculty.
Additionally, institutions must scrutinize third-party AI vendors to ensure compliance with security and ethical standards. AI applications used in research should undergo an Institutional Review Board (IRB) process to ensure ethical considerations are met, particularly when involving human subjects.
Implementing AI Risk Management
The widespread use of AI in higher education introduces risks, including bias in admissions, unfair grading algorithms, misinformation in student support chatbots, and data privacy breaches. A proactive risk management strategy is necessary to identify and mitigate these challenges before they affect students and faculty.
Universities should conduct regular AI risk assessments to evaluate whether AI models used in admissions and grading exhibit biases. Automated grading tools must be carefully monitored to ensure they maintain fairness and accuracy while respecting student privacy. AI-powered chatbots and virtual assistants should be assessed for misinformation risks to prevent the spread of inaccurate guidance to students.
To address AI-related risks, institutions should implement bias detection and mitigation tools, such as IBM AI Fairness 360 or Google’s What-If Tool, to analyze AI models for potential discrimination. AI decisions that impact student outcomes must be explainable, requiring institutions to audit AI-generated recommendations to ensure their legitimacy.
Data security is another critical component of AI risk management. Universities must encrypt AI datasets to protect student information and enforce strict access controls to prevent unauthorized use of AI-driven insights.
A well-defined AI incident response plan is essential for handling potential AI failures, such as erroneous grading, biased admissions decisions, or breaches of student data privacy. Universities must develop clear protocols for reporting AI-related incidents, conducting investigations, and implementing corrective actions.
Ensuring AI Compliance in Higher Education
To align AI usage with evolving legal and regulatory standards, institutions must ensure compliance with data protection laws such as FERPA in the U.S. and GDPR in Europe. These regulations require transparency in how AI processes student data, ensuring that personal information is protected from unauthorized access and misuse.
Title IX compliance is also a critical consideration. AI models used for student discipline or behavioral monitoring must be rigorously evaluated to prevent discriminatory decision-making. Universities should establish review mechanisms to ensure AI does not introduce biases that disproportionately impact certain student demographics.
For AI-driven research initiatives, compliance with Institutional Review Board (IRB) requirements is essential. Researchers using AI to analyze student behavior, health data, or academic performance must ensure ethical considerations are met and that data privacy is maintained.
When procuring AI solutions, universities should require vendors to adhere to compliance standards, such as ISO 42001 AI Management Standard. All third-party AI tools used for admissions, grading, or student services should be vetted for compliance with privacy laws and institutional policies before deployment.
Monitoring and Auditing AI Usage
To maintain accountability in AI-driven decision-making, universities must implement continuous AI performance monitoring. Establishing AI audit committees ensures that AI models used for admissions, grading, and student analytics are regularly reviewed for effectiveness, fairness, and ethical alignment.
Automated AI monitoring tools should be deployed to detect potential bias, model drift, or security vulnerabilities that could compromise AI’s reliability over time. Additionally, institutions should conduct annual audits of AI models, focusing on fairness and bias detection, ensuring that AI-driven decisions remain consistent and equitable.
Gathering feedback from students and faculty is also essential in assessing AI’s impact. Universities should establish feedback mechanisms that allow stakeholders to report concerns or inconsistencies in AI-generated outcomes. This feedback should be integrated into regular AI policy reviews, allowing institutions to adapt AI governance frameworks based on real-world insights.
To keep pace with evolving regulations and technological advancements, institutions must continuously update AI policies. As AI governance standards change, universities should revise compliance requirements and risk management strategies to ensure ongoing alignment with legal and ethical expectations.
Fostering an AI-Aware Culture
Successful AI adoption in higher education requires a culture that prioritizes responsible AI use and digital literacy. Universities should invest in AI education for faculty, staff, and students to ensure that all stakeholders understand the implications and limitations of AI technologies.
Training programs should be designed to help professors integrate AI into their teaching while maintaining academic integrity. Workshops on AI ethics and responsible AI usage should be offered to students, ensuring they are informed about AI-generated content, plagiarism risks, and AI’s role in decision-making processes.
Administrative teams, particularly those in admissions, HR, and IT, should receive training on AI compliance, risk management, and bias detection. Institutions should also foster ethical AI innovation by promoting research initiatives that align with the university’s values.
To encourage safe AI experimentation, universities can create AI sandboxes, providing controlled environments where faculty and students can explore AI applications while adhering to ethical and compliance guidelines.
Conclusion
As AI becomes an integral part of higher education, universities must balance innovation with ethics, fairness, and compliance. A well-structured AI Governance, Risk, and Compliance (AI GRC) framework ensures that institutions can harness AI’s benefits while mitigating risks related to bias, transparency, and data privacy.
By establishing clear governance policies, conducting rigorous risk assessments, ensuring compliance with legal standards, and maintaining ongoing AI monitoring, universities can deploy AI responsibly. Continuous training and a strong AI-aware culture will further support institutions in building trustworthy and transparent AI-driven ecosystems.
For CIOs and IT leaders in higher education, AI governance is more than a regulatory obligation—it is a strategic necessity that ensures academic integrity, institutional credibility, and a responsible approach to AI’s future in education.