Data Preprocessing and De-identification
Tokenisation and Encryption
Before feeding sensitive data into the LLM, implement tokenisation or encryption techniques to replace identifiable information with non-sensitive tokens. This preserves data utility while protecting individual privacy.
Data Masking
Apply data masking to hide or obfuscate sensitive elements within the dataset. This can include replacing names, addresses, or other personally identifiable information (PII) with pseudonyms or generic placeholders.
Architectural Safeguards
Data Privacy Vault
Implement a data privacy vault architecture to act as a privacy firewall between sensitive data and the LLM. This vault can:
- Detect and store sensitive information
- Replace sensitive data with de-identified versions
- Control access to re-identified data based on user permissions
Private LLM Deployment
Consider deploying a private LLM within a controlled environment to limit exposure of sensitive data to external parties. However, be aware that this alone does not solve all privacy challenges.
Access Control and Governance
Fine-grained Access Policies
Establish strict access control policies to ensure that only authorised users can interact with sensitive data or view re-identified information in LLM outputs.
Audit Logging
Implement comprehensive logging of all data access and LLM interactions to support compliance and enable thorough auditing.
Training and Inference Safeguards
Privacy-preserving Training
During model training, use the data privacy vault to de-identify sensitive information before it enters the training pipeline. This prevents the LLM from memorising specific sensitive details.
Inference Protection
Apply similar de-identification processes to user prompts and inputs during inference to prevent sensitive data from being inadvertently shared with the model.
Compliance and Best Practices
Regulatory Alignment
Ensure your data handling practices align with relevant privacy regulations such as GDPR, CCPA, and HIPAA.
Ongoing Monitoring
Continuously monitor LLM outputs for potential sensitive information leaks and implement feedback mechanisms to improve privacy safeguards over time.