The Add New AI Solution form allows you to register a new AI system in your organization’s inventory. This is the starting point for managing governance activities across the AI system's lifecycle, including risk assessments, dataset linking, policy enforcement, and audit readiness.
This form captures critical metadata about the system’s ownership, usage, architecture, risk posture, and value to the business.
Once submitted, the AI system will appear in your AI Inventory and can be managed through the AI System Details Dashboard.
Purpose
This form is designed to standardize how AI systems are cataloged across your organization. Capturing this information ensures traceability, facilitates risk and compliance reviews, and provides the foundation for effective AI governance.
It helps answer key governance questions such as:
What AI systems are in use?
Who is responsible for each one?
What models, vendors, and decision capabilities are involved?
What business value or risk does the system represent?
Field Descriptions
Below is a breakdown of each field, along with how it should be used:
Ownership and Structure
Solution Name – Name of the AI solution (e.g., Customer Recommendation Engine)
Solution Owner – Email of the individual responsible for this AI system
Organization Unit – Name of the owning department or business unit
Organization Subunit – Sub-team or division within the organization
Legal Entity – Formal legal entity responsible for oversight of this AI system
User / Customer – Internal customer or team the AI system serves
Stakeholders – Key decision-makers or oversight roles (e.g., CTO, Data Governance Lead)
Business Context
Uses – How the AI system is applied in operations or workflows
Purpose – Primary business purpose behind the system
Business Goals – Strategic business objectives the AI supports
Business Value – Expected business value or outcomes from using the AI system
Risk and Classification
Risk Tier – Risk classification level (Low, Medium, High) based on internal criteria
Open Source – Toggle to indicate whether this system is open source
AI Model Types (optional) – Architecture type(s) used (e.g., LLM, NLP, Computer Vision)
Technical and Operational Context
Business Processes – Business function this AI supports (e.g., invoice matching, HR screening)
Platform – Core AI platform in use (e.g., ChatGPT, AWS SageMaker, Azure ML)
Source Type – Where the system originated (e.g., vendor-built, internal development)
Vendor Name (optional) – Vendor or provider of the AI model or service (e.g., OpenAI, Microsoft)
Technical Documentation Link(optional) – URL to product or system documentation
Consumption Method (optional)– How the AI is accessed or consumed (e.g., API, UI, SDK)
Environment (optional) – Deployment context (e.g., Production, Staging, Development)
Watermarking Technique (optional) – Method used for output tracking (e.g., C2PA, synthetic tagging)
Version Control Method (optional) – How model or version control is maintained
Last Training Date (optional) – Most recent training date for the model
Build Date (optional) – Date when the system was first deployed or built
Lifecycle Stage (optional)– Current phase: Development, Testing, Production, or Retired
Decision Capability (optional) – Whether the AI makes autonomous decisions (e.g., advisory only, full automation)
Notes
This form must be completed before proceeding with Risk Assessments, SBOM, or TEVV.
Some fields are optional, but capturing as much context as possible improves audit readiness and system oversight.
Submitted systems can be edited later through the AI System Details page.
Organizations that have upgraded to the Teams or Enterprise plans gain access to Shadow AI Discovery tools, which allow your organization to automatically detect shadow AI within your ecosystem.