Anthropic Data Usage Policies for Commercial and Consumer Products

Data Usage Policy for Commercial Customers

By default, Anthropic does not use inputs or outputs from its commercial products—such as Claude for Work, the Anthropic API, and Claude Gov—to train its AI models. This default protection is designed to maintain the privacy and confidentiality of enterprise and developer workflows.

However, this policy changes when a user explicitly chooses to provide feedback. If a user utilizes the thumbs up or thumbs down buttons to report bugs or provide feedback, Anthropic reserves the right to use the entire related conversation for research, analysis of service effectiveness, and model training. When this occurs, the data is de-linked from specific user and customer IDs before being processed.

Feedback Data Handling and Retention

When a user submits feedback, the entire conversation—including custom styles, preferences, and model settings—is stored in a secure back-end for up to five years. While this feedback does not include raw content from external connectors (such as Google Drive or local MCP servers) by default, any data explicitly copied into the chat session may be included in the submission.

For organizations using Team or Enterprise plans, the Primary Owner or Owner has the ability to disable the feedback mechanism entirely. This is managed through the Organization settings under the "Data and Privacy" section, specifically by toggling the "Rate chats" setting.

Community Perspective and Concerns

Discussions surrounding these policies highlight a tension between the need for product improvement and user privacy expectations. Many users express that they intentionally avoid using feedback buttons to prevent their data from being ingested into training pipelines, noting that the act of providing feedback is functionally equivalent to granting permission for data usage.

Some community members have raised concerns regarding "nagware" or automated prompts for feedback in CLI tools like Claude Code. There is ongoing debate about whether dismissing these prompts or providing quick, forced responses could inadvertently lead to data being flagged for training. As one observer noted:

"I literally think differently and self-censor when I think my work is being surveilled or stolen, which makes me less productive."

Conversely, other users argue that the feedback loop is a necessary component of product development. The consensus among many technical users is that providing a transcript is the only practical way for developers to debug model performance issues, making the data usage a logical, if sometimes undesirable, trade-off for better model alignment.