What governance mechanism ensures customer data shared with a third-party LLM complies with data privacy policies?

The governance mechanism that ensures customer data shared with a third-party LLM (Large Language Model) complies with data privacy policies is Data Masking and Zero Retention.

Data masking involves the process of obscuring specific data within a database to protect it while still maintaining its usability for analysis. This is crucial for compliance with data privacy regulations, as it helps prevent personally identifiable information (PII) from being revealed or misused when data is shared externally.

Zero retention complements data masking by ensuring that no customer data is permanently stored by the third party. This means that once the data has been processed, it is deleted immediately, further minimizing the risk of unauthorized access or data breaches. Together, these practices not only safeguard customer privacy but also align with legal and regulatory frameworks that demand stringent data protection measures.

In contrast, the other options focus on different aspects of data handling and security. Secure Data Retrieval emphasizes the secure access to data rather than the data itself being protected once shared. Dynamic Grounding relates to integrating factual data into models rather than managing data privacy. Prompt Injection Defense is about securing the interaction between users and the model rather than focusing on the governance of shared data. Thus, Data Masking and Zero Retention are the most effective practices to