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Rubrik Gains Access to Anthropic Mythos Research Preview to Advance Cyber Resilience

Barchart·06/02/2026 09:30:00
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Rubrik (NYSE: RBRK), the Security and AI Operations Company, today announced it has been granted access to Anthropic’s Mythos Research Preview as part of Project Glasswing. An important next step in the evolution of AI and cybersecurity, Rubrik is testing this advanced frontier AI defensively to identify, vet, and patch potential software vulnerabilities across the company’s enterprise platform and products suites.

Anthropic is giving access to Mythos Research Preview to organizations that build or maintain critical software infrastructure for the purposes of finding and fixing vulnerabilities in advance.

“It’s important for us to be a part of Anthropic’s Glasswing project as we work to speed trusted AI transformation for our customers, which includes both trust in our software and in our ability to deliver preemptive recovery and resilience,” said Bipul Sinha, CEO, Chairman and Co-founder of Rubrik. “By putting Anthropic’s model to work directly on our code, we are proactively purging potential vulnerabilities before they can ever be leveraged against us.”

Claude Mythos Research Preview validates that the speed of discovery of software vulnerabilities is a risk that needs to be taken seriously. Anthropic is helping its partners to plan ahead, to be preemptive, which is to vet their systems and apply fixes that position them best for future recovery and resilience.

For more information on Rubrik joining Anthropic’s Project Glasswing, read Rubrik’s blog post here.

About Rubrik

Rubrik (NYSE: RBRK), the Security and AI Operations Company, leads at the intersection of data protection, cyber resilience, and enterprise AI acceleration. Rubrik Security Cloud delivers complete cyber resilience by securing, monitoring, and recovering data, identities, and workloads across clouds. Rubrik Agent Cloud accelerates trusted AI agent deployments at scale by monitoring and auditing agentic actions, enforcing real-time guardrails, fine-tuning for accuracy, and undoing agentic mistakes.

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