June 18, 2026

ZetaChain unveils Anuma, a private memory layer for AI

ZetaChain has launched Anuma, a consumer app that aims to address growing privacy concerns surrounding artificial intelligence (AI) platforms, which often collect and control personal user data. With OpenAI and Google’s Gemini amassing millions of users, the AI industry has faced backlash over issues of data privacy, with a recent survey indicating that 90% of users are worried about data collection practices. Anuma utilizes a unique encrypted memory vault that allows users to retain their personal context across different AI models, thus mitigating the fragmentation commonly experienced when switching between assistants. This new approach aligns with a broader movement towards encrypted, user-managed alternatives in AI, indicating a significant shift in how users interact with and control their data in the rapidly evolving technology landscape.

ZETA: ZETA functions as the native token powering the ZetaChain network and its AI applications. It supports access to models and agents, on-chain settlement, storage operations, and network security. In the reported development, ZETA underpins the economic and functional elements of the private memory layer and Anuma.
Anuma: Anuma serves as the initial consumer application built on ZetaChain to manage encrypted personal memory vaults for AI conversations. It supports importing histories from various models and enables seamless switching while keeping data private and portable. The app illustrates ZetaChain’s approach to building practical tools on its AI-focused infrastructure.
ZetaChain: ZetaChain operates a Layer 1 blockchain network now focused on delivering a private, user-owned memory layer for AI systems. The project provides infrastructure for encrypted context, identity, permissions, payments, and agents that can move across different models and apps. In this news, ZetaChain is advancing its AI consumer layer through its reoriented stack and the launch of consumer-facing tools.

Privacy: Widespread user concerns over AI platforms collecting and controlling personal data are prompting interest in encrypted, user-managed alternatives.
Consumer Layer: Projects are exploring unified stacks that combine memory, agents, and payments to reduce reliance on centralized model providers.
Interoperability: Fragmentation across AI models creates challenges for developers and users seeking consistent context and permissions across different providers.

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