Managing AI Systems in AI Governance
An AI System in AI Governance is an object type to assist organizations in capturing essential details on regulatory-mandated information, promoting, compliance, and facilitating easier audits. Given that AI Systems have a unique lifecycle that differs from traditional IT assets, and that different systems vary in their levels of autonomy and adaptiveness after deployment, this inventory can track version changes, model updates, and performance shifts over time.
To create an AI system
To manage AI System inventory details
To manage AI system workflows
To delete an AI System
FAQ
When should I use the AI System Object in AI Governance?
OneTrust recommends creating an AI System in AI Governance when developing, deploying or using an AI solution for an approved use case.
Using the AI System object enables tracking and management of an AI system's lifecycle, including design, deployment, and ongoing monitoring. The AI System object allows you to document and manage the AI system's key details, including its purpose, design specifications, compliance with regulatory requirements, ethical considerations and performance. It also facilitates continuous monitoring and risk management throughout the AI system’s operational life, ensuring transparency, accountability, and alignment with governance policies.
If I have AI solutions, when should I use the Project Object?
OneTrust recommends using a Project over AI System when:
- Exploring a new use case
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that will likely involve AI as part of the solution.
- Acquiring an asset
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that integrates AI in its operation or functionality.
Using the Project object helps facilitate due diligence by allowing you to assess the use case through a governance lens. It supports the consideration of regulatory requirements, potential risks, and ethical implications before implementation. The Project object also enables you to document objectives, privacy, security, and ethical concerns, capture results from initial experiments or proof-of-concepts (POCs), and evaluate the data being used, even in the early stages of project approval.
How does an AI System differ from an Asset?
An AI System refers to a specific solution that is designed to perform a task or solve a problem using AI. It includes the technical details of the AI itself, such as models, datasets, training processes, and performance metrics.
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Examples: Customer-facing chatbots, production recommendation engines, automated decision systems, deployed computer vision systems, operating fraud detection models
An Asset refers to a broader resource or technology that is used or acquired by the organization, which may or may not involve AI. Assets can be hardware, software, data sets, third-party products, or services.
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Examples: Databases, cloud computing platforms, content management systems, productivity platforms
How should I use AI Systems with Projects and Assets?
Let’s use an example scenario.
If planning to leverage DataGuidance Copilot, create an Asset record to track the use of DataGuidance technology within your organization. Link the OneTrust Vendor record to this Asset as OneTrust is the provider. Link your organization as the Entity that will be deploying the product/service.
Then create a Project record in AI Governance to assess the DataGuidance Copilot product offering for understanding and documenting it’s capabilities, risks, adherence to regulatory requirements, enforcing implementation of controls, privacy, security and ethical considerations, etc. Ensure to link the DataGuidance Asset record to this project.
If the AI Governance committee approves the use of DataGuidance Copilot, you have the ability to promote this Project record to an AI System and track it throughout it’s deployment lifecycle and post deployment for monitoring. The Project, Asset, Vendor and Entity records gets linked to this AI System automatically upon promotion.