# 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](https://doc.dastra.eu/en/features/ai-systems/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, 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. *** [risk-analysis-and-business-value](https://doc.dastra.eu/en/features/ai-systems/risk-analysis-and-business-value "mention")\ \ [import-your-ai-systems](https://doc.dastra.eu/en/features/ai-systems/import-your-ai-systems "mention") [transparency-notice](https://doc.dastra.eu/en/features/ai-systems/transparency-notice "mention") [ai-models-repository](https://doc.dastra.eu/en/features/ai-systems/ai-models-repository "mention")