Crossposted using Lemmit.
Original post from /r/privacy by /u/shekar_h on 2023-07-07 05:01:47+00:00.
Large Language Models (LLMs), like GPT-4 by OpenAI, have various applications, spanning from interactive public models to privately hosted instances for businesses. Each application brings forth its unique data protection and privacy compliance concerns.
This thread explores various scenarios related to data protection considerations that companies are exploring. If you are aware of anything besides what I have listed below, then feel to respond to this thread:
1. Using Public LLMs
Application: Public models, such as ChatGPT, are used in various contexts due to their versatile capabilities.
Example: An individual might use ChatGPT online to ask general questions or gather information on a topic.
Data Protection Consideration: When interacting with public models, the data shared might be exposed to third parties. Employees might inadvertently share sensitive data, which can significantly impact the brand and business. Privacy compliance could be at risk if personal or proprietary information is shared. Users must exercise caution to mitigate this risk.
2. Hosting Private Instances
Application: Businesses may host private instances of LLMs for internal use, such as managing corporate knowledge.
Example: A company may use a privately hosted LLM to automate responses to frequently asked internal questions about compliance policies and procedures.
Data Protection Consideration: Hosting LLMs privately reduces the risk of external data leaks.
3. Fine-tuning Public Models
Application: Fine-tuning a public model for a specific task, like customer support.
Example: An organization may fine-tune ChatGPT on its product-specific data to provide automated customer support.
Data Protection Consideration: While the risk of data leakage to the outside is relatively low, data might be exposed inadvertently during the model's interaction with internal users. Exposing customer information, salary, or sensitive business data can lead to serious issues. Therefore, businesses must establish strict data management practices and privacy compliance protocols during fine-tuning and deployment.
4. Using Applications that Employ LLMs
Application: Tools or platforms that use LLMs for tasks
Example: An app that uses an LLM to help users write essays or reports.
Data Protection Consideration: The risk of data leakage varies depending on whether the application uses public, private, or fine-tuned LLMs. As a general rule, assuming a high level of risk is advisable. Applications must implement stringent data privacy measures and ensure robust security practices to uphold privacy norms.
In summary, navigating the data protection and privacy compliance concerns that come with the versatility of LLMs is crucial. Whether an organization uses public models, hosts private instances, fine-tuning models, or employs LLM-powered applications, robust data management strategies and strict compliance protocols are essential. That said, managing these complexities can be challenging. Hence, to help organizations leverage LLMs more securely and responsibly.