Cerebras Partners with Armis: Using Ultra-Fast AI to Accelerate Application Security Vulnerability Remediation
Cerebras and Armis have announced a partnership that combines Armis Centrix™ for Application Security with Cerebras' ultra-fast AI inference capabilities to help teams identify and remediate vulnerabilities faster. Armis launched this AI-driven application security platform on February 10, 2026; it can scan code, dependencies, container images, and configuration files, and the company says it achieved the highest performance in the third-party Public CASTLE Benchmark C@250.
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Mar 27 2026
At Cerebras, we’ve always believed that speed changes what’s possible. In software development, that means more than faster generation or faster inference. It means faster iteration, faster validation, and faster action. That’s why we’re excited to spotlight Armis, whose Armis Centrix™ for Application Security unifies application security across the software lifecycle. With Armis and Cerebras, teams can identify and remediate vulnerabilities faster while reducing noise and focusing on the risks that matter most.
The timing matters. Armis launched Armis Centrix™ for Application Security on February 10, 2026, positioning it as an AI-powered platform for detection, contextualization, and remediation across the software development lifecycle. In its launch materials, Armis argued that AI-assisted coding and continuous development pipelines are exposing the limits of fragmented AppSec point tools: too much noise, too little context, and too much friction between security and development.
That problem is only getting bigger. Armis says AI-generated code is increasing the speed and scale at which vulnerabilities can be introduced, while traditional tools often struggle to catch novel variants or connect findings to what is actually exploitable in production. Armis Centrix™ is designed to scan across code, dependencies, container images, and configuration files, while also understanding the broader CI/CD pipeline and production-side controls. The goal is not just to find more issues, but to surface the right issues sooner and make them easier to fix.
This is where Cerebras fits naturally. Cerebras is built for ultra-fast AI and instant developer workflows. On our platform, we talk about instant answers, “code at the speed of thought,” and low-latency AI experiences that help teams stay in flow. When AI can respond in near real-time, the value is not just developer productivity. It is the ability to shorten the entire loop between detection, understanding, and remediation. That same principle is what makes this Armis partnership so compelling.
What does that unlock in practice?
We also like that Armis is bringing measurable validation to the table. The company says Armis Centrix™ for Application Security achieved the highest performance in the Public CASTLE Benchmark C@250, a third-party benchmark for detecting and stopping code issues before deployment. In a category crowded with static, fragmented tools, that kind of signal matters.
More broadly, this partnership reflects a shift we see across the industry: AI-assisted development is compressing the time between idea and deployment, so security has to operate with the same urgency. Faster models alone are not enough. Teams need application security that is contextual, automated, and embedded in the development workflow. Armis is building that security layer. Cerebras is built to support the speed modern AI workflows demand. Together, that is a powerful combination for teams that want to build fast without losing control of risk.
The future of software is not just faster code generation. It is faster, safer iteration. We’re excited to spotlight Armis because this is exactly the kind of partnership that helps make AI-native development more practical for the enterprise: less waiting, less noise, faster fixes, and more confidence from commit to production. Build and secure software faster—with Armis + Cerebras.
Performance comparisons are based on third-party benchmarking or internal testing. Observed inference speed improvements versus GPU-based systems may vary depending on workload, configuration, date and models being tested.
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