AI Risk Management Framework
A structured approach to identifying, assessing, mitigating, and monitoring AI risks, adapted from the NIST AI Risk Management Framework for the unique context of higher education, as defined in our AI Development Policy.
Seven characteristics of trustworthy AI
Following the NIST framework, Infinize defines trustworthy AI through seven characteristics that must be balanced across all AI systems.
Valid & Reliable
Accurate, consistent outputs across diverse institutions and student populations.
Safe
No conditions that endanger student welfare, outcomes, or institutional integrity.
Secure & Resilient
Confidentiality, integrity, and availability maintained under adverse conditions.
Transparent
Operations and outputs available to stakeholders with clear accountability chains.
Explainable
Mechanisms and outputs understood by advisors, faculty, and administrators.
Privacy-Enhanced
Student autonomy, identity, and data safeguarded with full FERPA compliance.
Fair
Equity promoted across demographic groups; harmful bias actively managed and mitigated.
Four-tier risk classification
Every AI system is classified based on potential impact, drawing on NIST AI RMF guidance and the risk categories defined in our AI Development Policy.
Prohibited
Subliminal manipulation, social scoring, real-time biometric identification. Must never be developed or deployed.
Full Governance
Predictive retention models, enrollment predictions, transfer evaluation, career recommendations. RASC approval and full TEVV required.
Standard Review
Course assistant, skills assessment, resume builder, dashboard analytics. RAWG review with standard TEVV and transparency notices.
Self-Assessment
Internal analytics, non-student-facing tools, data pipeline automation. Self-assessment by product team, registered in inventory.
Understanding AI risks in higher education
AI systems in higher education present unique risks that require careful management beyond traditional software risk frameworks.
Student Data Privacy
FERPA compliance across complex data flows involving student records, predictive models, and AI-generated recommendations
Equity & Fairness
Ensuring predictive models and recommendations do not perpetuate or amplify existing inequities across student demographics
Over-Reliance
Potential for automation bias among advisors and faculty who may defer to AI outputs without critical evaluation
Student Autonomy
Risk that AI nudges, alerts, or recommendations could unduly influence or constrain student agency and decision-making
GenAI Content Safety
Hallucination risks, inappropriate content generation, and prompt injection vulnerabilities in the Universal Assistant
Four NIST AI RMF functions
Not sequential steps but iterative, interconnected activities that span the entire AI lifecycle, adapted for the higher education context.
Culture & Policy
Cultivate a culture of AI risk management with policies, accountability structures, and organizational processes.
- Responsible AI Steering Committee (executive oversight)
- Cross-functional Working Group (operational risk assessment)
- Integration into product development lifecycle
- Ongoing institutional partner engagement
Context & Framing
Establish context for framing risks, intended purposes, potential impacts, stakeholder expectations, and institutional deployment settings.
- Document intended purposes and applicable laws
- Engage interdisciplinary teams with domain expertise
- Define organizational risk tolerances per institution
- Map third-party risks from LLM providers and integrations
Analyze & Monitor
Employ quantitative and qualitative tools to analyze, assess, benchmark, and monitor AI risk and related impacts over time.
- Track bias and fairness across diverse student populations
- Validate accuracy in different institutional contexts
- Monitor Universal Assistant for safety and appropriateness
- Assess whether features achieve educational outcomes
Respond & Treat
Allocate risk resources, develop treatment plans, implement incident response procedures, and maintain communication protocols.
- Prioritize risks based on student outcome impact
- Develop response plans for high-priority risks
- Maintain mechanisms to deactivate problematic systems
- Communicate incidents to institutional partners
AI system risk profiles
Specific risk profiles for each of Infinize's primary AI system categories, documenting risks, trustworthiness priorities, and key controls.
Predictive Analytics
Retention Risk · Enrollment Likelihood
Risks include bias in predictions across demographics, over-reliance by advisors, accuracy degradation, and privacy concerns with data aggregation.
Recommender Systems
Major · Career · Course Recommendations
Risks include reinforcing existing inequities in pathways, limiting student autonomy through narrow options, and insufficient diversity in outputs.
Universal Assistant
Generative AI Chatbot
Risks include generating inaccurate or harmful content, facilitating non-educational uses, exposing sensitive data, and providing inappropriate advice.
Alerts & Nudges
Intelligent Alerting System
Risks include alert fatigue, false positives creating anxiety, disproportionate alerting across groups, and undermining student autonomy.
Transfer Evaluation
Automated Credit Assessment
Risks include inaccurate credit equivalencies, bias against certain institution types, and lack of transparency in evaluation criteria.
Phased implementation
A structured roadmap for operationalizing the AI risk management framework across the organization.
Phase 1: Foundation
Months 1–3
Establish Responsible AI Steering Committee and Working Group. Conduct initial AI system inventory and risk categorization. Document risk tolerances and organizational policies. Begin GOVERN function implementation.
Phase 2: Assessment
Months 4–6
Complete MAP function for all deployed AI systems. Develop risk-specific measurement metrics and tools. Conduct initial bias and fairness audits across all predictive models. Establish third-party risk monitoring for LLM providers.
Phase 3: Operationalization
Months 7–12
Fully implement MEASURE and MANAGE functions. Deploy continuous monitoring and alerting for AI system performance. Integrate risk management into product development sprints. Publish transparency reports for institutional partners.
Phase 4: Maturation
Ongoing
Conduct periodic framework reviews and updates. Expand stakeholder engagement programs. Contribute to industry standards for AI in higher education. Develop institution-specific risk profiles for all partner institutions.
Want a deep dive into our risk framework?
Our team can walk you through every dimension of how we identify, assess, and manage AI risks in education.