Back to List
Granola Privacy Alert: AI Notes Viewable via Link and Used for Training by Default
Industry NewsGranolaAI PrivacyData Security

Granola Privacy Alert: AI Notes Viewable via Link and Used for Training by Default

Users of the AI-powered note-taking application Granola are being advised to review their privacy settings following revelations regarding data accessibility and usage. Although the company markets its service as 'private by default,' the platform currently allows anyone with a specific link to view notes. Furthermore, Granola utilizes user notes for internal AI training purposes unless individuals manually opt out of the process. Positioned as an AI notepad for professionals, these default configurations have raised concerns regarding the actual level of privacy provided to its user base. This report explores the discrepancy between the marketing claims and the functional reality of Granola's data handling policies as reported by The Verge.

The Verge

Key Takeaways

  • Link Accessibility: Despite claims of being private, any individual possessing a specific link can view a user's Granola notes.
  • AI Training Defaults: Granola utilizes user-generated notes for internal AI training by default.
  • Opt-Out Requirement: Users must manually change their settings to prevent their data from being used for AI model development.
  • Privacy Discrepancy: There is a notable gap between Granola's "private by default" marketing and its actual data sharing and training configurations.

In-Depth Analysis

The Reality of 'Private by Default' Claims

Granola markets itself as an AI notepad designed for professional use, emphasizing a commitment to privacy. However, the current technical implementation reveals that notes are accessible to anyone who has the corresponding link. This configuration challenges the traditional definition of "private," as it relies on the secrecy of a URL rather than restricted access controls or authentication. For users handling sensitive professional information, this default state poses a potential risk if links are shared inadvertently or discovered by unauthorized parties.

Data Utilization for AI Development

Beyond the visibility of notes, Granola's policy regarding internal AI training has come under scrutiny. The platform automatically opts users into a program where their notes are used to train the company's internal AI models. While many AI companies seek user data to improve their algorithms, the integration of this as a default setting—combined with the link-sharing accessibility—highlights a trend in the industry where user data is a primary resource for product iteration. Users who wish to maintain total confidentiality of their notes must navigate the application's settings to explicitly opt out of these training protocols.

Industry Impact

The situation with Granola underscores a growing tension in the AI software industry between user privacy and the data requirements of machine learning. As more "AI-first" productivity tools enter the market, the definition of "private by default" is becoming increasingly fluid. This case serves as a significant example for the industry, suggesting that transparency regarding link-based sharing and AI training opt-outs is critical for maintaining user trust. It also highlights the responsibility of users to audit the privacy settings of AI tools, even when those tools are marketed as secure professional solutions.

Frequently Asked Questions

Question: Can anyone see my Granola notes without my permission?

Based on the report, anyone who obtains the specific link to your note can view its content, as this is the default setting for the application.

Question: Does Granola use my personal notes to train their AI?

Yes, Granola uses notes for internal AI training by default. Users must manually opt out if they do not want their data used for this purpose.

Question: How does Granola describe its own privacy policy?

Granola describes its notes as being "private by default," despite the link-sharing and AI training configurations currently in place.

Related News

Meituan LongCat Releases General 365: A Challenging New Benchmark for AI Reasoning Evaluation
Industry News

Meituan LongCat Releases General 365: A Challenging New Benchmark for AI Reasoning Evaluation

Meituan's LongCat team has officially open-sourced General 365, a new evaluation benchmark designed to measure the reasoning capabilities of large language models (LLMs). In a comprehensive test involving 26 mainstream models, the results revealed a significant gap in current AI reasoning performance. Even the top-performing model, Gemini 3 Pro, achieved an accuracy of only 62.8%, while the vast majority of tested models failed to reach the 60% passing mark. This release aims to establish a more rigorous standard for the industry, highlighting the current limitations of even the most advanced AI systems in complex reasoning tasks. By providing a transparent and difficult metric, Meituan seeks to drive the development of more logically capable artificial intelligence.

Managing AI Coding with Agent Evaluation Thinking: Meituan's Practice in Refactoring 310,000 Lines of Code
Industry News

Managing AI Coding with Agent Evaluation Thinking: Meituan's Practice in Refactoring 310,000 Lines of Code

As AI-generated code now accounts for over 90% of development in certain environments, the primary challenge has shifted from generation speed to the effective management and constraint of AI capabilities. Meituan's technical team recently shared their experience refactoring 310,000 lines of code using a strategy centered on "Agent evaluation thinking." By implementing technical debt assessment, standardized rules, a specialized Refactoring SOP, and a Pre-PR (Pull Request) mechanism, they have successfully transformed large-scale refactoring from a high-cost, periodic project into a continuous, daily operational task. This approach ensures that AI-driven development does not amplify systemic chaos but instead adheres to unified technical standards, maintaining long-term code quality and system stability in an AI-dominated coding era.

Meituan Technical Team Releases LARYBench: A New Benchmark for Universal Latent Action Representation in Embodied AI
Industry News

Meituan Technical Team Releases LARYBench: A New Benchmark for Universal Latent Action Representation in Embodied AI

The Meituan Technical Team has officially introduced LARYBench (Latent Action Representation Yielding Benchmark), a systematic evaluation framework designed to guide the learning of universal latent action representations from large-scale visual data. This benchmark marks a significant milestone in embodied AI by providing a standardized way to measure how models learn actions from visual inputs. Experimental results from the benchmark reveal that general vision models significantly outperform specialized embodied action expert models in both action generalization and control precision. Furthermore, the research demonstrates that embodied action representations can naturally emerge from large-scale human video data, suggesting that broad visual training is a viable path toward achieving more sophisticated and adaptable robotic control systems.