Hey everyone, I've been digging into NotebookLM's terms and privacy docs, and I think we need to have a clear discussion about data usage. A few members have sent me DMs expressing concern, specifically about whether the documents we upload—research, notes, proprietary drafts—are used to train the underlying models.
From my reading of their current policy, Google states that the content you upload is **not used to train their core, public AI models**. That's a crucial point. However, the data is used to "provide, improve, and develop" NotebookLM itself. This is a common, but often vague, area. The key question is: does "improve NotebookLM" mean our private documents could be used to train the *NotebookLM-specific* service? The policy says your data is isolated to your own notebook and not shared with other users, which is reassuring.
For any community members using this for sensitive material, I'd recommend a cautious approach. While the intent seems to be to keep your data compartmentalized, the long-term specifics of "development" can be ambiguous. I personally wouldn't upload truly confidential client data or unpublished proprietary work until we see more concrete, long-term track records or perhaps enterprise-grade data handling guarantees.
Has anyone seen a more detailed technical breakdown or had a different interpretation? Let's keep the discussion focused on the actual policy language and any verifiable experiences. It's a critical issue for evaluating this tool's fit for different workflows.
- Jordan
Happy to help.
The bit about data being "isolated to your own notebook" is the only thing that provides any comfort here. But that's a technical assurance about data *storage*, not data *usage*. It doesn't address the core ambiguity you flagged.
"Provide, improve, and develop" is the classic loophole. If my internal drafts are used to tweak a retrieval algorithm or tune a summarization feature for the service itself, that's still using my proprietary work to train something. It just isn't the foundational model. I'd need to see a categorical "not for training any model, full stop" before trusting it with anything sensitive.
Has anyone actually gotten a straight answer from them on what that "development" entails? My bet is they haven't defined it because they want the flexibility.
martech_auditor
Exactly. Your breakdown of "provide, improve, and develop" versus "isolated to your own notebook" is the critical tension. Even if the data is siloed for retrieval, the *operational telemetry* generated when you use that data - your queries, which summaries you accept or reject, how you interact with citations - that's a rich training dataset for the NotebookLM service layer.
That behavioral data, linked to your document content, could absolutely be used to "improve" features like answer relevance or summarization style. So while the raw text might not go into a base model, a derivative of your proprietary work could still inform their system's tuning. It's a more subtle form of training, but training nonetheless.
CompareKing
This "derivative training" point is super unsettling. Makes me wonder about our pitch decks. If we ask it to summarize a section about our proprietary tech, does that query + answer pair get logged? Even without the full doc, that's leaking intent and structure.
Is there any tool like this that DOES give a full opt-out on *all* training, even behavioral? Or is that just the cost of admission now?