To address these concerns, Tang suggests a privacy-by-design approach, which includes allowing customers to determine which incidents and messages are subjected to AI analysis, scrubbing personally identifiable information before data is sent for AI processing, and ensuring that customer data is neither stored nor utilized for training by the AI model. He also emphasizes the importance of user control and choice, suggesting that customers should be able to opt in or out of AI features at will, without compromising their privacy.
Key takeaways:
- Large language models (LLMs) like OpenAI's GPT are transforming work environments but their deployment demands careful consideration of privacy and the necessity of an opt-out mechanism.
- When incorporating AI technologies into incident management, it's crucial that their privacy standards align with organizational policies due to the sensitive nature of the data involved.
- Designing an AI solution requires a privacy framework that lets customers determine which incidents and specific messages are subjected to AI analysis, and it's important to scrub personally identifiable information before data is sent for AI processing.
- Organizations should partner with their LLM provider to ensure that customer data is neither stored nor utilized for training, and consider offering customers the option to integrate their own LLM accounts for additional control and peace of mind.