The framework also post-processes inference results before returning them to a client application such as a monitoring dashboard. The framework helps clean, filter, and pre-process the telemetry data before sending it to the NVIDIA Triton server for inference. It can run on site or in the cloud, and simplifies the analysis of logs and telemetry to help detect and mitigate security threats so that developers can create and deploy AI-powered security solutions more quickly. As a quick refresher, the NVIDIA Morpheus AI application framework is built on the RAPIDS libraries, deep learning frameworks, and NVIDIA Triton Inference Server. The software runs on Red Hat OpenShift and uses AI/ML to continuously inspect network and server telemetry at scale. NVIDIA's Morpheus AI Cybersecurity Framework is at the heart of the system. This architecture consists of several software and hardware components. NVIDIA Morpheus Early Access program (EAP) account.
Active Amazon Web Services (AWS) account.This article provides a set of configuration instructions to Red Hat developers working on applications that use the NVIDIA Morpheus AI application framework and NVIDIA BlueField data processing units (DPUs) to secure interservice communication. Red Hat and NVIDIA have been working together to bring the security analytics capabilities of the NVIDIA Morpheus AI application framework to Red Hat infrastructure platforms for cybersecurity developers. As a consequence, the ability to track, monitor, and secure a datacenter in a timely manner has risen above that of any individual or team, thus requiring the help of AI and machine learning ( AI/ML) to enable ITOps, infrastructure security, and DevSecOps teams to manage the complexity of modern cloud-native applications and the underlying platforms. The growth of cloud-native applications has driven an explosion of east-west network traffic within a datacenter where applications can create hundreds of thousands of network connections among virtual machines and containers.
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