1.11 – GPT Privacy And Usage Lesson

Lesson Overview

Data privacy is a common concern when using custom GPTs. Entrepreneurs want to know if OpenAI or others can access the information shared through these tools. This lesson explains what information is exposed, when, and to whom, helping you decide how to manage your privacy. You'll see what OpenAI has made public about handling chat data, particularly for custom GPTs that are either shared publicly or used privately.

Custom GPTs offer settings at both the GPT and account level to help manage what is used for model training and what is visible in your chat history. We review the difference between public and private GPTs—especially regarding copyright checks and data exposure—and look at practical steps to reduce the risk of sensitive data leaking.

For those with higher privacy requirements, this lesson touches briefly on alternatives like Microsoft Azure’s implementation of OpenAI models, which offer greater controls but are more complex to set up. Understanding these options helps you match your use case with the appropriate tool: if you need quick and collaborative AI, standard GPTs may be suitable; if you need confidential and controlled processing, other solutions should be considered.

Who This Is For

Anyone looking to manage sensitive data in custom GPTs will benefit from this lesson.

• Entrepreneurs working with proprietary or client information
• Educators or trainers concerned with privacy in classroom tools
• Consultants who may handle confidential business data
• Business owners deploying custom GPTs for internal use
• Product managers assessing the security of AI integrations
• Anyone curious about data privacy limitations in public AI platforms

Where This Fits in a Workflow

Understanding and configuring privacy settings should be one of your first steps before deploying a custom GPT—either internally or externally. After setting up your custom GPT, but before sharing access, review the available privacy controls. For projects involving multiple users or sensitive content, you’ll revisit these settings whenever usage or team needs change.

For example, you may create a public knowledge-base GPT for customer support and need to check copyright and privacy controls, or you may internally deploy a private GPT where turning off training data becomes necessary. This lesson helps you ensure you’re meeting your team’s or clients’ privacy standards as your GPT usage grows.

Technical & Workflow Benefits

By learning about and applying the correct privacy settings, you sidestep risks that come with the default or manual approach. In the past, users may not have realized their chat data was being used for further model training, or which parts of the GPT could expose confidential details. With today’s structured privacy controls, you can selectively turn off features—like model improvement using your chats or broader chat history—to achieve a more secure working environment.

For example, disabling training data ensures that business conversations aren’t fed into OpenAI’s future model improvements. At the same time, knowing that chat data is not shared with builders boosts confidence when deploying GPTs across teams or with external users. Compared to unstructured or manual management, these controls enable faster setup with more predictable, compliant privacy boundaries.

Practice Exercise

Choose a custom GPT you’ve deployed or are considering for your business.

1. Open the GPT’s settings and review the privacy options under the configure tab, noting whether conversation data is set to be used for model improvement.
2. Access your main OpenAI account settings and check the data controls related to chat history and training data. Observe how changes affect both history and privacy.
3. Consider: If you turned off all sharing and training, would any aspect of your daily workflow or collaboration be negatively affected? Which balance works for your needs?

Reflect on any trade-offs between privacy and convenience when configuring these settings.

Course Context Recap

This lesson addresses privacy and usage in custom GPTs, following the previous discussions on data security and privacy measures. Previously, you learned about the basics of data handling in OpenAI tools. Up next, we move into hands-on configuration or usage scenarios to deepen understanding. Explore all lessons to build a workflow that’s secure, effective, and right for your entrepreneurial objectives. Further insights and practical demonstrations are available throughout the course.