Security best practices for LLM integration
To secure data privacy in LLM integrations, we can use encryption for data at rest and in transit, anonymize sensitive information, and enforce robust access controls. In this section, we will learn how to implement data minimization, secure sharing practices, and implement differential privacy. We will also go through the importance of regularly auditing for compliance, integrating security across the development life cycle, establishing firm data retention rules, and providing continual security training for staff.
Data privacy and protection
Ensuring the security of LLMs during integration into systems involves a comprehensive approach to data privacy and protection. Here are detailed best practices for securing LLM integrations:
- Encryption:
- At-rest encryption: All sensitive data stored for LLM use should be encrypted. This includes training data, model parameters, and user data. Techniques such as Advanced Encryption Standard...
