What feature in the Einstein Trust Layer ensures that sensitive information such as personally identifiable information (PII) is hidden during AI processing?

Data Masking is the correct choice because it is specifically designed to protect sensitive information such as personally identifiable information (PII) by obscuring or transforming the data during AI processing. When data masking is applied, the original values are replaced with fictitious yet realistic values or altered in a way that maintains its utility for processing while preventing unauthorized access to sensitive details.

In the context of the Einstein Trust Layer, data masking plays a critical role in ensuring that AI models can still function effectively without compromising the data privacy rights of individuals. This allows organizations to leverage AI technologies while adhering to data protection regulations and minimizing the risk of data breaches.

The other features mentioned do not primarily focus on the preservation of sensitive information during processing. For instance, toxicity detection relates to identifying inappropriate or harmful content but does not relate directly to how sensitive data is handled. Zero retention involves not keeping any data after processing, which is also essential for privacy but does not inherently mask data during use. Auditing serves to track access and modifications to data, which is important for compliance but does not prevent sensitive data from being exposed during processing.