Which AI Tools Protect Your Data Best? New AI Privacy Ranking for 2026

Generative AI has become a standard part of everyday work within just a few years. Employees use ChatGPT, Claude, Gemini, Copilot and Perplexity to prepare content, analyse documents, conduct research, work with data and communicate with customers. As AI adoption increases, however, one question is becoming increasingly important for companies: What actually happens to the information we enter into AI systems? A new 2026 study by Incogni compared 13 leading generative AI platforms in terms of privacy protection and the way they handle user data. The results reveal substantial differences. Vibe by Mistral AI and ChatGPT by OpenAI ranked among the best-performing tools, while Microsoft Copilot, Meta AI and Kimi appeared at the opposite end of the ranking. However, this does not mean that any AI platform is completely free of privacy risks.

The study compared 13 AI platforms

Incogni researchers analysed:

• ChatGPT – OpenAI
• Claude – Anthropic
• Gemini – Google
• Grok – xAI
• Vibe, formerly Le Chat – Mistral AI
• Perplexity – Perplexity AI
• Qwen – Alibaba
• DeepSeek
• Z.ai
• Kimi – Moonshot AI
• Meta AI
• Pi – Inflection AI
• Copilot – Microsoft

The platforms were assessed according to 11 criteria across three main areas:

1. what happens to user data and conversations,
2. how transparently AI companies explain their practices,
3. what other personal data they collect and with whom it may be shared.

Under Incogni’s methodology, a lower overall score represented a lower privacy risk.

Lowest privacy risk: Vibe and ChatGPT

The best overall rating went to Vibe by Mistral AI, closely followed by ChatGPT.

Both platforms scored approximately 12 points, placing them in a separate group ahead of most competitors.

For ChatGPT, the study particularly highlights OpenAI’s transparency. According to Incogni, OpenAI provides comparatively clear information about privacy and explains relatively clearly how user data may be used.

ChatGPT users can also relatively easily disable the use of their conversations for future model training.

This is an important difference compared with some competing platforms, where a similar option may be harder to find or may require a separate request.

Highest privacy risk in the study: Copilot, Meta AI and Kimi

At the other end of the ranking were mainly:

• Microsoft Copilot
• Meta AI
• Kimi by Moonshot AI

The reason is not necessarily the AI service alone.

Large technology companies such as Microsoft and Meta operate extensive ecosystems, and their privacy policies often cover many different products at the same time.

This can make it more difficult for users to determine exactly which rules apply specifically to services such as Copilot or Meta AI.

Incogni notes, for example, that Microsoft may obtain information from data brokers and that the Copilot iOS app declares the use of certain data in connection with advertising partners.

According to the study, Meta’s mobile applications also collect one of the broadest ranges of user data.

Do AI companies use your conversations to train their models?

In many cases, they can.

The study indicates that consumer versions of AI services commonly use user interactions to improve or train models.

However, the type of account matters.

According to Incogni, many providers follow a model under which:

• standard consumer accounts may be included in model training,
• business, enterprise or API versions are generally excluded from this use by default.

According to the study, this approach is used by providers including OpenAI, Google, Microsoft, Perplexity AI and Mistral AI.

Companies should therefore not assume that an employee’s free personal account and a corporate AI service have the same data-handling conditions.

Training can often be disabled, but not always easily

Incogni also assessed how easy it is to prevent future conversations from being used for model training.

Relatively simple opt-out

Platforms with a relatively simple option include:

• ChatGPT
• Claude
• DeepSeek
• Vibe
• Grok
• Copilot
• Perplexity
• Pi

Users can prevent future conversations from being used for model training without a major loss of functionality.

Gemini

Gemini also allows users to limit the use of new conversations, although the setting is connected to Gemini Apps Activity. Disabling it therefore also affects conversation history.

More complicated procedures

Some platforms technically provide an opt-out mechanism, but using it may be considerably less convenient.

With Kimi, for example, users may need to contact support and verify their identity. For some other services, the study did not identify a clear and simple mechanism at all.

Data already used for model training may be difficult to remove

One of the most important findings is easily overlooked in discussions about AI privacy:

Disabling model training mainly affects future data.

According to Incogni, none of the analysed platforms provides users with a simple way to remove their information from a model once it has already been used for training.

For companies, this leads to a straightforward rule:

Employees should not enter information into public AI tools that should remain inside the company unless the organisation clearly understands the conditions and settings of the account being used.

This may include:

• confidential quotations,
• contracts,
• customers’ personal data,
• internal financial data,
• business strategies,
• source code,
• internal documentation,
• confidential product information.

AI models also work with publicly available information

The study highlights another area that is important not only for privacy but also for AI Visibility.

Almost all analysed providers state that publicly available information is used in some form when creating their models.

This can include website content and information from social networks.

According to published information, Grok uses public posts on X, while Meta uses public content from Facebook and Instagram. Other providers refer to combinations of public, licensed and private datasets.

At the same time, virtually no provider publishes a complete list of the datasets used to train its models.

Incogni identifies this lack of transparency as one of the significant shortcomings of today’s AI ecosystem.

Public information about companies is becoming increasingly important in the AI era

For businesses, there is another side to the issue.

While confidential information needs to be protected, companies increasingly need to publish the information that should be publicly accessible in a clear and structured way.

For example:

• what the company does,
• what products it manufactures,
• what services it provides,
• which types of customers it serves,
• which markets it operates in,
• what expertise it has,
• what technologies it uses,
• how it differs from competitors.

AI assistants are increasingly being used to search for suppliers, partners, products and services.

A company with only fragmented, outdated or inconsistent information available online may therefore be considerably more difficult for AI systems to identify and understand.

However, an important distinction must be made:

The fact that website content may appear in an AI model’s training data does not automatically mean that the company will be highly visible in answers generated by ChatGPT, Gemini, Claude or Perplexity.

AI Visibility also depends on the availability of current information, its structure, source credibility, context and the ability of AI systems to correctly associate a company with specific products, services or industries.

Companies need two parallel strategies

Generative AI creates two requirements that may initially appear contradictory.

1. Protect information that should remain private

Companies should define which information employees may enter into AI systems and which AI products can be used when working with sensitive data.

For intensive corporate use, a business or enterprise environment with clearly defined data-handling terms will generally make more sense than employees relying on free personal accounts.

2. Actively publish information that should make the company discoverable

At the same time, companies should ensure that public information about their products, services and expertise is:

• current,
• accurate,
• unambiguous,
• sufficiently detailed,
• easy for machines to interpret,
• consistent across different sources.

This area is becoming the basis of a new discipline known as AI Visibility, GEO (Generative Engine Optimization) or LLM optimization.

Key takeaways from the study

The Incogni study demonstrates significant differences between generative AI platforms in their approach to privacy.

In the 2026 ranking, Vibe by Mistral AI and ChatGPT by OpenAI performed best, with ChatGPT standing out particularly for the transparency of information relating to the handling of user data.

No AI platform, however, is entirely free of privacy risks.

Companies therefore increasingly need to distinguish between two categories of information:

Data that should remain within the company needs to be protected from inappropriate use in AI systems.

Information that should help AI systems find and recommend the company to potential customers should instead be systematically and professionally published.

Generative AI is therefore changing both the rules of corporate data protection and the rules of online business visibility.

Source

This article is based on the Gen AI and LLM Data Privacy Ranking 2026 published by Incogni. The study analysed 13 AI platforms against 11 criteria related to user-data usage, transparency and sharing of personal information. Data collection took place between 15 June and 6 July 2026.