AI Classes
Taxonomy of AI applications
Classification of AI applications
The stated goal of the SENSIBLE-KI project is to secure embedded and mobile AI applications. In order to ensure a standardized security protection, it is necessary to systematize AI applications. The specific needs for protection can then be determined by means of discrete AI classes.
Based on the evaluation of a wide range of AI applications, the following classes were identified in this project. It is a vertical classification which is based on different properties of the AI applications.
The individual protection needs can be determined by categorizing the application with these different levels.
Source of Input Data | |
|---|---|
| Where does the input data come from? | |
| Class 1: | explicit user input |
| Class 2: | implicit user input (Tracking) |
| Class 3: | sensory data |
Type of Input Data | |
| What is the format of the input data? | |
| Class 1: | image |
| Class 2: | audio |
| Class 3: | text |
| Class 4: | other |
Personal Reference | |
| Does the input data contain sensitive information? | |
| Class 1: | non-critical |
| Class 2: | indirect personal reference |
| Class 3: | direct personal reference |
Processing of Input Data | |
| Is the Input Data processed and if yes, how? | |
| Class 1: | no |
| Class 2: | yes, automatically |
| Class 3: | yes, manually |
Preparation of Input Data | |
| How is the input data prepared? | |
| Class1: | data cleansing |
| Class 2: | anonymization |
| Class 3: | feature engineering |
Training Time | |
| When and how often is the model trained? | |
| Class 1: | model is trained once (offline learning) |
| Class 2: | model is trained continuously (online learning) |
Training Location | |
| Where is the model trained? | |
| Class 1: | decentralized und decoupled between different devices |
| Class 2: | decentralized, peer-to-peer |
| Class 3: | centralized on a server |
| Class 4: | federated |
Deployment | |
| Are there vulnerable communication paths? | |
| Class 1: | Applications which are deployed on a device and don't have to communicate with a server |
| Class 2: | Applications which use a model on a server |
| Class 3: | Applications which get their model from a server |
Type of Model | |
| What is the structure of the model? | |
| Class 1: | classical (transparent) machine learning algorithm |
| Class 2: | neural networks |
Protection Measures | |
| Which measures have been taken? | |
| Class 1: | software measures |
| Class 2: | hardware measures |
| Class 3: | both |
| Class 4: | neither |
Type of Output | |
| What is the model's task? | |
| Class 1: | classification |
| Class 2: | regression |
| Class 3: | data creation |
Last modified April 2, 2024: minor change of Best Practices' description (1df7aed)