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
Create a record of AI Systems
To add an AI system, click "Create a new AI system". A window offers three creation modes:
Generate with AI — The AI assistant automatically generates a complete record from a short description (name, publisher, URL…). It uses web browsing to enrich the information.
Built-in template — Choose from the Dastra AI system library or from another workspace.
Personalised — Create a blank record and fill in each field manually.
Generate an AI system with the AI assistant
Select "Generate with AI", then describe the system to document (name, publisher…). You can attach a file or provide a URL to help the generation.
The assistant produces a pre-filled record (name, description, learning type, systemic risk, publisher…) which you can review and validate before saving.
Data generated automatically by AI may contain errors. Review and correct the proposed record before saving it to your registry.
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. The same organisation may hold several roles simultaneously:
Provider — develops an AI system and places it on the market or puts it into service under its own name or trademark.
Deployer — uses an AI system under its authority in the course of professional activities.
Importer — imports an AI system from a third country for placing on the EU market.
Distributor — makes an AI system available on the EU market without being a provider or importer.
Authorised representative — a natural or legal person in the EU mandated in writing by a provider established outside the EU.
Product manufacturer — if an AI system is integrated into a product subject to sector-specific legislation (e.g. medical devices), the product manufacturer is treated as a provider for that system.
Contextual alerts based on declared role
When declaring the Deployer role, Dastra displays contextual alerts based on the system's risk level:
Unacceptable risk — If you declare yourself as the deployer of an unacceptable-risk system, an alert reminds you that deploying such a system is prohibited under the AI Act.
High risk — If you declare yourself as the deployer of a high-risk system, an alert informs you of the situations where a Fundamental Rights Impact Assessment (FRIA) is mandatory, and redirects you to the relevant questionnaire.
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.
Linked processing activities and data synchronisation
The Data processings tab of an AI system record lets you associate one or more processing activities from your GDPR record with that system. This link keeps your processing register and your AI systems registry consist
Restore a previous version
The Activity history panel on an AI system's record keeps every creation and modification. You can restore a past version directly from this panel.
Each history entry shows a restore button, visible to users with write permission. After confirmation, Dastra creates a new AI system from the data of the chosen version; its name receives the suffix "- restored version - [date]" and you are redirected to this new record. The original AI system remains unchanged.
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