AI Governance and Monitoring Platform

The Vendor is required to provide comprehensive AI governance and monitoring solution, to include but not limited to:
•    Software
•    Implementation services including training, documentation, and knowledge transfer
•    Ongoing support
- AI inventory and registration management:
•    AI use case intake and approval workflow
o    Intake form requiring documentation of applications, training data sources, model type, intended users, and data sensitivity. 
o    Configurable workflow engine that facilitates communication and coordinated action among diverse stakeholders.
•    Automated and manual AI system registration. 
•    Risk classification (e.g., low, moderate, high, critical)
o    Risk scoring logic that flags AI systems using phi and student data for additional review. 
o    Support for risk-based AI classification.
•    Model documentation repository
o    Dashboard showing all active AI tools by department and risk tier
•    Version tracking and lifecycle status (development, validation, production, retired).
- Policy management and governance framework:
1. The following capabilities are considered required:
•    Policy mapping to AI systems.
•    Control validation for AI-specific risks (bias, data leakage, trust, privacy, security)
•    Policy attestation and user acknowledgment tracking via structured signoffs, attestation, and approval requirement.
•    Automated enforcement.
2. The following capabilities are suggested:
•    Controls requiring human-in-the-loop review for high-risk AI decisions.
•    Enforcement requiring bias testing documentation prior to production deployment.
•    Mapping institutional AI policy controls to federal and state regulations.
- Risk assessment and compliance monitoring:
1. The following capabilities are considered required:
•    AI risk assessment templates.
•    Frameworks to classify, assess, and mitigate AI specific risks and content libraries to address regulations.
•    Bias detection and fairness monitoring.
•    Data drift and model performance monitoring.
•    Provide documentation for trust, risk, and security assessments, testing, and validated results, risk and compliance remediation evidence.
•    Provides comprehensive audit trails of actions taken in the platform and automatically logs activities related to the AI life cycle.
•    Provide regulatory alignment – since we have diverse use cases – like
2. The following capabilities are suggested:
•    Ongoing monitoring of predictive analytics models for performance degradation.
•    Alert when model output accuracy drops below predefined threshold.
•    Periodic bias audit reports for admissions or hiring-related AI tools.
- Data privacy and security oversight:
1. The following capabilities are considered required:
•    Integration with IAM systems (e.g., SSO, role-based access).
o    Role-based access controls and SSO integration capability.
•    Logging and activity monitoring.
•    Data classification alignment.
•    Data access control and retention policies.
•    Third-party AI risk evaluation support.
2. The following capabilities are suggested:
•    Automated notification if AI model accesses restricted phi datasets.
•    Dashboard showing AI systems interacting with confidential research data.
•    Integration with GRC tools for unified risk view.
•    Vendor risk management integration.
- Monitoring of generative AI and external AI tools:
1. The following capabilities are considered required:
•    Monitoring of enterprise-approved generative AI tools.
•    Shadow AI discovery.
•    Prompt logging and usage analytics.
•    Data loss prevention alignment.
•    Analytics dashboard showing departmental usage trends.
2. The following capabilities are suggested:
•    Monitoring API usage volume and sensitive data submissions.
•    Alerts for excessive upload of sensitive institutional data into external AI platforms.