Securing generative AI: data, compliance, and privacy considerations

The article discusses key considerations and best practices for securing generative AI across different scopes, ranging from consumer applications to self-trained models. It also covers regulatory themes around data privacy, transparency, human oversight, risk classification, and safety.

Specifically, the article covers:

• Scope 1 (Consumer applications): Understand service provider's terms, data sources, and output implications. Consider having a process to monitor policy changes and restricting sensitive data use.
• Scope 2 (Enterprise applications): Negotiate terms, understand data flow and residency, consider opt-out for data reuse in model training, and protect API keys.
• Scope 3 (Pre-trained models): Understand model feedback mechanisms, indemnification policies, data usage, and implement output validation.
• Scope 4 (Fine-tuned models): Examine fine-tuning data sources, restrict access based on data classification, and be cautious about using sensitive data.
• Scope 5 (Self-trained models): Communicate data usage via EULA, avoid training on sensitive data unless required, govern the model per regulatory requirements, and limit user data storage.
• Regulatory themes: Data privacy, transparency and explainability, human oversight, risk classification of AI systems, and safety considerations.
• Conclusion: Apply existing data governance and handling policies, train users, assess third-party data sources, and monitor evolving regulations.