As generative AI systems become embedded in daily life, data privacy has emerged as a critical concern for users and regulators alike. In 2025, the challenge isn’t just whether AI can perform it’s whether it can do so without exploiting your personal information. As you have noticed there are a lot of privacy issues with the rapid expansion and goals of hitting AGI. Sam Altman just said to the NY Times that he believes the NYT doesn’t like people’s privacy as they want OpenAI to preserve all logs. To say the least it was a bit of tension to kick off the interview. So below we breakdown the privacy rankings.
The 2025 AI Privacy Rankings: Which GenAI Tools Protect Your Data Best?
To help clarify how leading platforms handle user data, Incogni conducted a comprehensive analysis of the most widely used GenAI and LLM platforms, ranking them based on 11 weighted privacy criteria. The findings, released in their “AI and LLM Privacy Ranking 2025,” highlight wide disparities between platforms that embrace transparency and those that treat user data as an afterthought.
Why AI Privacy Matters More Than Ever
Today’s AI platforms don’t just answer questions or write code—they often store, analyze, and retrain on user data. With many platforms harvesting inputs to refine their models, the boundary between helpful AI and invasive surveillance is increasingly blurred.
Users, for the most part, don’t understand what data is being used, how it’s stored, or who might have access to it. Given the lack of clear regulatory guidance in many jurisdictions, users are left to rely on vague privacy policies that require a law degree to interpret. Incogni has put together a detailed piece and the graph above of platform privacy rankings putting Meta AI at the bottom 3 spots below DeepSeek and Mistral AI at the top right above OpenAI.
Ranking the AI Privacy Landscape
Incogni’s analysis evaluated AI tools across three categories:
- Model Training Practices
- Transparency and User Awareness
- Data Collection and Sharing
Each category included criteria weighted by significance. For example, whether user prompts are used to train models, and whether those prompts are shared with third parties, were given more weight than general data collection.
The Top Performers: Le Chat, ChatGPT, and Grok
Le Chat (Mistral AI) earned the highest privacy score. Though it scored modestly on transparency, it excelled in limited data collection and user training opt-out options. This French-based tool proved the least privacy-invasive overall.
ChatGPT (OpenAI) followed closely. It was the most transparent platform, clearly stating how user data is handled and offering users the ability to opt out of model training. ChatGPT’s privacy policy is also one of the most readable in the industry.
Grok (xAI) ranked third. It fared well on opt-out functionality but lost ground due to concerns around data transparency and the extent of its data collection.
The Biggest Offenders: Meta AI, Gemini, and Copilot
Platforms from Big Tech fared significantly worse. Meta AI landed at the bottom of the list, with Gemini (Google) and Copilot (Microsoft) not far behind. Key concerns included:
- Vague or overly broad privacy policies
- Difficulty in opting out of data training
- Extensive third-party data sharing
These platforms often collect precise location, contact details, and usage data, particularly through their mobile apps. Worse, some share this data with advertising networks or within their corporate ecosystems.
Transparency in Name Only
While some platforms claim to prioritize user privacy, Incogni’s researchers found that many lack accessible, plain-language explanations of their data handling. In several cases, privacy policies were buried within general corporate disclosures, making it difficult to distinguish how AI-specific data is handled.
Microsoft, Meta, and Google were called out for vague descriptions and privacy documents that attempt to cover all products under one umbrella. This makes it challenging for users to understand what specific data their AI interactions generate or where it ends up.
Training Data: Opt-Outs Are Rare
Although a few platforms—like ChatGPT, Grok, and Mistral—allow users to opt out of using their data for training, most do not. Platforms like Meta AI and Gemini provide no clear mechanism for opting out, and some, like DeepSeek and Pi AI, were found to share inputs within their corporate group or with loosely defined affiliates.
The lack of user control over training inputs not only violates emerging privacy norms but also exposes companies to regulatory risk, especially in regions enforcing GDPR or state-level U.S. privacy laws.
App Privacy: Mobile Risks Amplify the Problem
Incogni’s researchers didn’t stop at web platforms—they also evaluated mobile app behavior. Unsurprisingly, the apps often collect more data than their desktop counterparts.
Meta AI, Gemini, and Claude stood out for collecting sensitive data like:
- Exact location
- Photos and media access
- Contact details and phone numbers
Many of these data points are also shared with third parties, often with limited disclosure.
Conversely, Le Chat and Pi AI offered more minimal data collection practices on mobile, enhancing their privacy scores.
Toward a Privacy-Conscious AI Future
The Incogni report makes one thing clear: privacy is not a default setting in generative AI—it’s a design decision. Users must be vigilant, and companies must be pressured to create AI that respects fundamental rights.
In the absence of global AI-specific privacy regulations, reports like these are essential for transparency and accountability. They empower consumers, shape regulatory discussions, and force vendors to justify their data practices.
As GenAI becomes central to work, communication, and creativity, it’s time for privacy to become a first-class priority—not an afterthought hidden in the fine print.
Who can you trust with your data in 2025? According to Incogni, very few—but some are trying more than others. For AI Privacy Compliance assistance for your organization book a demo with our privacy and compliance superhero team using the link below.
