One of the most powerful ways to make open-weight models safer is simply to remove dangerous information (e.g., anthrax papers) from their pre-training data. This is not yet common practice because developers are extremely reluctant to modify their expensive and proven pre-training recipes.

To use AI agents securely, avoid granting them full access to your sensitive data. Instead, create a separate, partitioned environment—like its own email or file storage account. You can then collaborate by sharing specific information on a task-by-task basis, just as you would with a new human colleague.

Enabling third-party apps within ChatGPT creates a significant data privacy risk. By connecting an app, users grant it access to account data, including past conversations and memories. This hidden data exchange is crucial for businesses to understand before enabling these integrations organization-wide.

Even with contractual promises from tech giants, the history of the internet suggests that "privacy is a game." For corporations with sensitive information, the only certain method to prevent data from being shared or used for training other models is to not share it in the first place, driving demand for on-prem solutions.

To lower the activation energy for user adoption, OpenAI deliberately will not use data connected to ChatGPT Health to train its foundation models. This strategic choice is designed to remove any tension between privacy and utility, assuring users their sensitive information is not being used for other purposes and building the trust necessary for scaled impact in the healthcare domain.

Treat ChatGPT like a human assistant. Instead of manually editing its imperfect outputs, provide direct feedback and corrections within the chat. This trains the AI on your specific preferences, making it progressively more accurate and reducing your future workload.

For security-conscious organizations, using external LLMs to process confidential data poses inherent risks. Building a walled-off, in-house LLM provides a secure alternative for internal knowledge management and AI tooling, as AvePoint did with its "Chat AVPT."

Major AI chatbots are designed with a default setting that opts users *into* having their conversations—including sensitive data—used for model training. This "opt-out" privacy model places the burden on the user to navigate settings and protect their own data, a critical fact many are unaware of.

When companies don't provide sanctioned AI tools, employees turn to unsecured public versions like ChatGPT. This exposes proprietary data like sales playbooks, creating a significant security vulnerability and expanding the company's digital "attack surface."