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Home » OpenMatter Network Introduces Verifiable Trust Layer for Secure Collaboration and AI Agents
Press Release

OpenMatter Network Introduces Verifiable Trust Layer for Secure Collaboration and AI Agents

By News RoomJune 30, 20264 Mins Read
OpenMatter Network Introduces Verifiable Trust Layer for Secure Collaboration and AI Agents
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MELBOURNE, Fla., June 30, 2026 (GLOBE NEWSWIRE) — OpenMatter Network today announced the launch of its cryptographically verifiable platform for secure collaboration and AI governance, built on a simple premise: Don’t Trust Data. Prove It.

For decades, organizations have relied on trust-based assumptions to secure data, execute workloads, and govern digital systems. But as data becomes increasingly distributed and AI agents begin operating autonomously across organizations, applications, and networks, those assumptions are being tested in new ways.

Enterprises today face a growing challenge: they must be able to collaborate, compute and deploy AI systems across environments they do not fully control. In this new reality, trust alone is no longer sufficient. Organizations need the ability to verify how data is used, how computations are executed, and how AI systems behave.

OpenMatter Network was created to address this challenge through mathematical proof and cryptographic verification rather than assumption-based trust models. The company believes verification is becoming a foundational requirement for secure collaboration, governed AI systems, and the next generation of digital infrastructure.

Positioned as the Verifiable Trust Layer for Secure Collaboration and AI Agents, OpenMatter Network enables organizations to securely collaborate, execute sensitive workloads, and deploy AI systems across environments that cannot be inherently trusted.

Designed to complement existing enterprise infrastructure, OpenMatter Network integrates with the cloud, data, and AI platforms organizations already use. Rather than replacing existing investments, the platform adds a layer of cryptographic verification, governed execution, and secure collaboration across environments that cannot be inherently trusted.

The platform combines advanced cryptographic verification, enforceable policy controls, and distributed architecture to create a verifiable foundation for collaboration, computing, and AI execution across organizational boundaries.

“For decades, organizations have been asked to trust the systems they rely on,” said Renee Davis, CEO and Co-Founder of OpenMatter Network. “We believe the next generation of digital infrastructure will be built on proof. Organizations need the ability to mathematically verify how data is used, how computations are executed and how AI systems behave. They don’t need to replace the infrastructure they already rely on; they need the ability to verify what happens across it. That is the problem OpenMatter Network was built to solve.”

The OpenMatter Network platform includes several core technologies designed to address the growing need for verifiable collaboration and governed AI infrastructure.

Masked Compute enables mathematically verifiable execution for secure collaboration and computation across organizations without exposing sensitive underlying data.

QuantumGuard provides verifiable policy enforcement and governance for AI agents operating across systems and environments.

Datavizor serves as the verification and visibility layer, providing insight into cryptographically provable execution, policy enforcement, and AI activity.

Together, these technologies create a unified platform for secure collaboration and governed AI execution at a time when enterprises are increasingly concerned about the operational, regulatory, and security risks associated with uncontrolled AI systems and distributed computing environments.

“Organizations are quickly realizing that policy prompts and assumed compliance are not enough,” said Ada Anderson, CTO and Co-Founder of OpenMatter Network. “If you cannot prove what happened, you cannot truly govern it.”

Davis and Anderson bring extensive experience in secure systems architecture, distributed computing, AI infrastructure, and cryptographic technologies. Their combined backgrounds helped shape the OpenMatter Network architecture around the growing operational realities facing organizations deploying AI and distributed computing systems at scale.

The platform is designed to support a wide range of enterprise and research applications, including healthcare collaboration, secure AI model training, financial analytics, enterprise AI governance, and distributed scientific research.

OpenMatter Network is already collaborating with Dara, a privacy-first health data platform, to explore how verifiable collaboration infrastructure can support healthcare insights without compromising individual data privacy.

“The future of healthcare depends on our ability to learn from data without compromising the privacy of the individuals behind it. By combining Dara’s privacy-first health data platform with OpenMatter Network’s secure collaboration infrastructure, we are exploring new ways to generate insights while keeping users in control of their information. We are excited about what this collaboration can make possible,” added Alanna Scott Delaney, CEO of Dara.

To learn more about OpenMatter Network, users can visit https://www.openmatter.network/.

For the press kit, users can click here.

About OpenMatter Network
Headquartered in Florida’s Space Coast, OpenMatter Network is building the Verifiable Trust Layer for Secure Collaboration and AI Agents. Guided by the principle “Don’t Trust Data. Prove It.,” the company’s cryptographically verifiable architecture enables secure collaboration, governed AI behavior and mathematically verifiable execution across untrusted environments. For more information, users can visit www.openmatter.network.

Contact

Account Supervisor
Caleigh McDaniel
Griffin360
[email protected]

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/5b037663-a2c0-429f-b293-b5ae53e2b133

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