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 August 25, 2022: update KI-Klassen (2999eeb)