Establishing a record of AI systems
To set up a record of your AI systems, you have two options:
Either you already have a registry that you can import directly into Dastra (go to the next section: Import your AI systems)
Or you don't have one. In this case, you'll have to create one yourself
Import you AI Systems
If you don't already have an AI system registry, skip ahead to the next section: Create a record of AI Systems.
You can easily upload your existing register directly into Dastra. This saves you having to fill in everything by hand.
To do this, access the list view, entitled "AI systems". In the top right-hand corner, open the drop-down menu next to the "Create a new AI system" button, then click on "Import". A new page appears, at the bottom of which you can add your existing record.
We recommend that you follow the steps on the Import your data (Excel, Csv, JSON) page for further details.
Create a record of AI Systems
To add an AI system, first click on "Create a new AI system". A window will appear, in which you must enter the name of the system and assign it to an organizational unit.
Once you've entered the required information, you'll be redirected to a 10-step form. This form will enable you to give as much detail as possible about the AI system.
The 11 Steps of the AI System Form
Below are the 11 steps to complete when documenting an AI system.
1. General
Enter basic information about the AI system:
Name of the system
Brief description of its purpose and functionality
2. Responsibilities
Define your role and responsibilities under the European AI Act:
Provider: develops and places the AI system on the market
Deployer: uses the AI system within professional activities
3. AI Models
Specify the AI model(s) used to process data within this system.
ℹ️ For more details, refer to the AI Models Repository.
4. Stakeholders
Identify stakeholders involved in implementing and managing this AI system, including their roles (e.g. Data Scientist, DPO, Product Owner).
5. Assets
Add the assets supporting this AI system, such as:
Infrastructure components
Software tools
APIs
Documentation resources
6. Datasets
List the datasets associated with this AI system. Indicate their usage among the following phases:
Training: the dataset used to train the AI model, enabling it to learn patterns, relationships, or classifications based on historical data.
Validation: a separate dataset used to tune model parameters and prevent overfitting. It helps assess model performance during training and guides adjustments for optimal results.
Testing: another distinct dataset used to evaluate the final performance of the trained and validated model before deployment. It provides an unbiased measure of how the model will perform on new, unseen data.
Production inference: data processed by the AI system during actual operation, where the trained model generates predictions, classifications, or decisions in real-world scenarios.
Ensure that each dataset’s purpose, composition, and linkage to this AI system are clearly documented for transparency and compliance purposes.
7. Data Subjects
Specify the categories of data subjects whose personal data is processed by the AI system (e.g. customers, employees, users).
8. Risk Analysis
Assess the level of risk based on:
Types of data processed
Processing activities
Potential impacts on individuals’ rights and freedoms
9. Business Value
Determine a business value score reflecting the system’s contribution to your organization to:
Prioritize high-value projects
Align AI initiatives with strategic objectives
10. Documentation
Attach relevant documents and information leaflets, such as:
User notices
Technical guides
Compliance assessments (e.g. DPIAs)
11. Summary
Review a comprehensive summary of all information entered for this AI system before final validation and registration.
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