Privacy and Property

Mitigation strategy

1. Implement a comprehensive 'Privacy by Design' framework, beginning with strict data minimization principles by only collecting and retaining data absolutely necessary for the LLM's function, coupled with automated, real-time data masking or Personally Identifiable Information (PII) scrubbing before data ingestion and inference to ensure de-identification. 2. Enforce a robust, policy-driven access control model utilizing Role-Based Access Control (RBAC) and Multi-Factor Authentication (MFA) for all interactions with the LLM system and its underlying data stores. This access architecture should adhere to the Principle of Least Privilege and be integrated into a Zero-Trust framework that verifies every access request regardless of source. 3. Ensure all sensitive data—including training data, prompt logs, and model weights—is protected through industry-standard encryption both at rest and in transit. Concurrently, apply data protection mechanisms such as differential privacy or machine unlearning techniques during the training and fine-tuning phases to significantly mitigate the risk of data memorization and subsequent direct or indirect leakage.