A trust boundary refers to a clear distinction between the parts of a system that are trusted to behave correctly and securely and those that are not. This distinction is essential in AI systems, as it helps ensure that sensitive information and critical decisions are made by trusted components of the system while reducing the risk of bad actors compromising the system or making incorrect decisions.
Examples of trust boundaries in AI systems include:
- Data processing: Trust boundaries can be established to ensure that sensitive data is only processed by trusted system components and is not accessed or manipulated by external parties.
- Model training: Trust boundaries can be established to ensure that machine learning models are only trained on trusted data and that outside parties do not compromise the training process.
- Model inference: Trust boundaries can be established to ensure that machine learning models are only used for prediction and decision-making purposes by trusted components of the system and that untrusted parties do not manipulate the predictions made by the models.
Establishing and maintaining trust boundaries in AI systems is essential for ensuring their reliability and security and for maintaining the trust of users and stakeholders in the system.