What is an Asset?

An Asset represents an AI system or AI use case in the platform.

It is created by grouping related Artifacts such as repositories, datasets, model endpoints, documents, and logs that together form a single AI system.

An asset does not replace the underlying Artifacts. Instead, it provides a structured view of how those Artifacts work together.

Artifacts are the raw ingredients, and an Asset is the finished product. A single AI system often leaves traces in multiple places. There might be training code in a repository, a deployed endpoint in the cloud, documentation on an internal wiki, and experiment logs in an ML platform. Each of those traces is an Artifact. When grouped together, they form one Asset: the AI system itself.

What information does an Asset contain?

• Identity - Name, description, owner, business unit, and current status of the AI system
• Linked Artifacts - All the individual evidence items connected to this AI system, traceable back to their original Sources
• Risk profile - Governance and technical risk scores from assessments, giving you a clear picture of the risk this system carries
• Tasks and workflows - History of governance actions taken including assessments, tests, mitigations, and reviews
• Actions - Outstanding follow up items that need attention, such as failed assessments or pending reviews
• Logs - A complete audit trail of everything that has happened with this Asset from the moment it was created

How Assets are created

Assets can be created in two ways:

Through Reconciliation - When the platform groups related Artifacts together automatically or with guidance from your team, the resulting grouping becomes an Asset. This is the most common path for Assets discovered through AI Discovery

Manual registration - Your team can also create Assets manually for AI systems that may not have been discovered through scanning, such as third party vendor AI or systems in environments not yet connected as Sources

Why Assets matter for governance

Governance doesn't happen at the Artifact level. You don't assess a single script or a single configuration file in isolation. You assess the AI system as a whole. Assets give your governance, compliance, and risk management teams a single place to understand everything about an AI system: what it is, where it came from, what risks it carries, and what actions have been taken.

Every assessment, test, mitigation, and compliance workflow in the platform runs against an Asset. It is the central object that ties everything together.

Asset lifecycle

Assets are not static. They move through a lifecycle as governance activities are performed:

1. Created through Reconciliation or manual registration

2. Risk mapped to understand the governance and technical risks involved

3. Assessed and tested using the platform's task catalog

4. Mitigated where risks are identified and need to be addressed

5. Monitored on an ongoing basis to ensure continued compliance and performance

6. Signed off when governance requirements are satisfied