How to Build a University AI System Registry: Template and Guide

Core registry outcomes

A useful registry gives university governance, security, privacy, procurement, and operational teams a shared record for accountability, risk, agent permissions, and audit evidence.

Define the registry as a control system, not a spreadsheet

The registry becomes operational when it is tied to workflow. Intake alone creates a list. Governance requires decision points, evidence collection, runtime oversight, and lifecycle maintenance. The workflow should apply to new systems and to existing AI uses discovered through procurement, IT review, surveys, cloud logs, or departmental attestations.

Minimum AI system inventory template

The inventory should capture enough context to support ownership, review, operational monitoring, and auditability without relying on informal documentation.

• System identity: Registry ID, system name, description, lifecycle state, deployment environment, and whether the entry is an AI tool, embedded feature, model, agent, workflow, or dataset dependency.
• Ownership: Institutional owner, technical owner, contract or vendor owner, data steward, privacy reviewer, security reviewer, and escalation contact.
• Purpose and users: Approved use case, prohibited uses, user groups, impacted populations, student-facing status, employee-facing status, and whether outputs influence decisions or assessments.
• Data and integrations: Data categories, sensitivity level, source systems, downstream systems, APIs, files, retrieval sources, logging destinations, and external exposure.
• Model, vendor, and dependencies: Provider, model or service type, hosted or self-managed deployment, third-party dependencies, model update responsibility, data processing terms, and incident notification path.
• Risk and evidence: Risk tier, approval status, required controls, evidence links, reassessment date, material-change history, owner attestations, incidents, and retirement records.

Classify risk using higher education impact, not only technical severity

Risk classification should reflect the university context. Relevant dimensions include data sensitivity, user impact, autonomy, model provenance, external exposure, and access to institutional systems.

Governance workflow from intake to retirement

The registry should connect the full lifecycle: intake, review, approval, runtime oversight, reassessment, material-change review, and retirement.

1. Register and classify the AI system

   Record the system identity, ownership, purpose, users, affected populations, data access, integrations, vendor or model dependencies, risk tier, and required controls.

2. Collect review and approval evidence

   Connect intake to privacy, security, procurement, data governance, and operational review decisions, including approval status, exceptions, reassessment dates, and required remediation.

3. Monitor operational use

   Maintain visibility into access changes, activity records, policy enforcement events, incidents, owner attestations, and evidence from source systems.

4. Reassess, review material changes, or retire

   Keep the registry current as systems change, risks evolve, vendors update models, integrations expand, agents gain permissions, or institutional use ends.

Connect the registry to runtime evidence

A registry is most valuable when it does not rely entirely on manual updates. Descriptive metadata should live in the registry, while evidence should be linked from authoritative systems. Procurement records can show vendor approval and contract ownership. IAM can show account, group, and permission assignments. Cloud logs and SIEM events can show system activity and security-relevant behavior. Ticketing and GRC systems can show review history, exceptions, incidents, and remediation. Data catalogs can help identify source datasets and sensitivity categories.

Audit records should preserve the basics needed for accountability: event type, date and time, source, outcome, and identities associated with the event. For AI agents, evidence should also capture the agent identity, delegated user identity model, tool calls, approval checkpoints, policy decisions, and whether actions stayed within defined scope. This is especially important for agents that can invoke functions, interact with other agents, or operate through MCP-style tool interfaces.

The registry should enforce role-based access because entries may reveal sensitive integrations, data categories, vulnerabilities, vendors, and control gaps. Governance leaders need broad visibility, but departmental owners, reviewers, auditors, and operators may need different views. Separating metadata from evidence also helps reduce overcollection in the registry while preserving auditability through controlled links to source systems.