Privacy advocate **Moxie Marlinspike**, known for creating the secure communication app **Signal** and its widely used open source encryption protocol, announced this week that his privacy-focused AI platform, **Confer**, will begin integrating its technology into **Meta**’s AI systems. Every day, billions of chat messages sent through **Signal**, **Meta**’s **WhatsApp**, and **Apple**’s Messages are protected by end-to-end encryption. This feature, which prevents tech companies and others from snooping on messages, has become mainstream. However, as generative AI platforms gain popularity, billions of daily interactions with AI chatbots lack this protection, making them accessible to AI firms. # The Push for Privacy-Focused AI Platforms often train AI models on extensive user data, making it difficult to opt out. As chatbots and AI agents become more capable, technologists and companies are pushing for more constrained and privacy-focused systems. “As LLMs continue to be able to do more, we should expect even more data to flow into them,” Marlinspike wrote in a blog post about the collaboration with **Meta**. “Right now, none of that data is private. It is shared with AI companies, their employees, hackers, subpoenas, and governments. As is always the case with unencrypted data, it will inevitably end up in the wrong hands.” Marlinspike stated he will “work to integrate Confer’s privacy technology so that it underpins Meta AI.” He emphasized that **Confer**, which debuted earlier this year, will operate independently of **Meta**. The project aims to provide technology that “allows everyone to get the full power of AI along with the full privacy of an encrypted conversation.” # Marlinspike's History with Meta In 2016, Marlinspike collaborated with **WhatsApp**, owned by **Meta**, to implement end-to-end encryption for over a billion accounts. Recently, **WhatsApp** introduced a **Meta AI** chatbot that lacks the same protection as individual chats. **WhatsApp** head Will Cathcart stated on X about the collaboration with **Confer**: “People use AI in ways that are deeply personal and require access to confidential information. It's important that we build that technology in a way that gives people the power to do that privately.” # Challenges and Future Directions The adoption of encrypted AI is still in its early stages. Existing cryptographic schemes for traditional digital communication don’t easily translate to generative AI data protection. **Confer** is a new project, and specific details about the collaboration with **Meta** are still emerging. **Mallory Knodel**, a cryptography researcher at New York University, believes it would be “great for people using chatbots that use Meta AI to have confidentiality and privacy within that exchange.” This would mean **Meta** couldn't access AI chat data for training, according to Knodel, who recently published a study on end-to-end encryption and AI. “I really hope more AI chatbots adopt this approach.” Knodel’s initial assessments suggest that **Confer** isn’t perfect but is an important step toward building a private AI chatbot. Cryptographer **JP Aumasson**, the chief security officer at Taurus, also views **Confer** positively: “Confer is probably the best private AI solution, all things considered. It's not perfect, of course. It lacks documentation of its architecture, threat model, and supply chain. But Moxie knows what he's doing and has a solid track record.” Developing encryption schemes for AI platforms is complex. Much of the privacy work has focused on accessible open source models or creating privacy layers between AI companies and end users. Marlinspike noted that “Confer’s technology has been built on top of open weight models. While many people love using Confer for a wide variety of tasks, others have missed the frontier capabilities from proprietary models.” The collaboration with **Meta** offers Marlinspike the opportunity to work directly with closed models. “Meta is building advanced frontier models, so this will combine the most private AI chat technology in the world with the most capable AI models in the world,” he wrote. Researchers emphasize the significance of this collaboration, regardless of whether it achieves all its goals. Taurus’ Aumasson concludes: “Moxie's proposal of using trusted computing, a concept dating back at least to the 1990s, is sound to me. The underlying assumptions and limitations are well understood. Again, it's not perfect, but probably sufficient for most users. The challenge is to support models that are as good as the latest frontier models from Anthropic and Google and OpenAI.”