LLMs rely on a prompt that takes user data and generates output based on that. Sometimes we may have to pass some sensitive data to LLMs to get the desired output.
Sharing proprietary code or internal data with LLMs may expose serious risks of data security and IP protection. This article outlines the key risks associated with exposing sensitive information to AI models and how organizations can protect themselves while still reaping the benefits of AI.
🔍 How do you expose data to AI?
Sensitive data can be exposed to various GenAI tools, platforms, or via APIs:
- Copy-pasting code into tools like ChatGPT or GitHub Copilot and platforms like Cursor.
- Uploading documents or datasets to AI-powered platforms.
- Using third-party AI APIs without governance.
- Fine-tuning or training models using internal data without proper safeguards.