Privacy Leakage
The model is trained with personal data in the corpus and unintentionally exposing them during the conversation.
ENTITY
2 - AI
INTENT
2 - Unintentional
TIMING
3 - Other
Risk ID
mit31
Domain lineage
2. Privacy & Security
2.1 > Compromise of privacy by leaking or correctly inferring sensitive information
Mitigation strategy
1. Implement rigorous **Data Redaction and Sanitization** protocols, utilizing techniques such as masking, tokenization, or k-anonymization, to remove all Personally Identifiable Information (PII) and confidential data from the training corpus prior to model ingestion. 2. Apply formal privacy mechanisms, such as **Differential Privacy (DP)**, during the model training or fine-tuning process to mathematically bound the maximum risk of extracting sensitive information stored within the model parameters. 3. Institute a run-time system of guardrails and output filters, including real-time PII detection and **Data Loss Prevention (DLP)**, on all model outputs to prevent the unintentional disclosure of sensitive information during the conversational inference stage.