Introduction

A DOCA service running on the NVIDIA BlueField networking platform, to immediately detect, and allow responding to, attacks minimizing their potential impact and risk.

The DOCA Argus framework provides real time situational awareness and runtime threat detection by inspecting host memory using advanced memory forensics. Live machine introspection is performed at the hardware level, analyzing specific snippets of volatile host memory, to monitor threats in real time without impacting system performance. DOCA Argus does not violate privacy as information is extracted from kernel structures only.

Unlike conventional tools, Argus runs independently of the host, requiring no agents, integration or reliance on host-based resources. This agentless, zero-overhead design enhances system efficiency and ensures resilient security in any compute environment, including bare-metal, virtualized, containerized and multi-tenant infrastructures. By operating outside the host, isolated, in its own trust domain, DOCA Argus remains invisible to attackers — even in the event of a system compromise.

Cybersecurity professionals can seamlessly integrate DOCA Argus with their SIEM, SOAR and XDR security platforms, enabling continuous monitoring, incident response, and automated threat mitigation while extending their existing cybersecurity capabilities into other environments (i.e., AI infrastructure).

NVIDIA BlueField is a foundational security component for every AI factory and AI cloud, providing built-in, data-centric protection for AI workloads at scale. By combining BlueField’s acceleration capabilities with DOCA Argus’ proactive threat detection, cloud service providers and enterprises can secure AI factories without compromising performance or efficiency.

A single BlueField card with DOCA Argus is to monitor an entire node.

Installation and Getting Started

All preparation steps are listed under DOCA's Container Deployment User Guide. Note: The DOCA Service container is configured for K8S-based deployment, hence the use of the docker pull command is discouraged.

Note: The DOCA Service container is configured for K8S-based deployment, hence the use of the docker pull command is discouraged.

Preparation steps for the DOCA Service As explained in the service's DOCA Argus Documentation, there are some resource allocation steps needed before the service can be executed. It is recommended to go over the Container Deployment User Guide and follow the instructions that match the DPU on which the service will be deployed.

Adjusting the .yaml configuration The .yaml configuration for our container is doca_argus.yaml.

Note: The file is stored with the rest of the .yaml configurations as were pulled from NGC in the previous steps (See \"Installation and Getting Started\").

The main environment variables that should be adjusted according to the setup are the following:

SERVICE_CONFIG_FILE - full service configuration file - Options are either a \"default\" config file or customized config file placed under /etc/argus or the yaml content string (presented by default in the downloaded yaml) Configuring telemtry FluentBit is integrated into the Argus service telemetry system to handle the export of telemetry data. This integration ensures that logs and metrics are efficiently collected and forwarded to the desired destinations for analysis and monitoring.

The integration from Argus service to FluentBit is locally, after that FluentBit can be configured to send the data to end destination or be a proxy for another FluentBit with additional security among them.

Check resources page for FluentBit configuration examples

Spawning the container Simply copy the doca_argus.yaml file to the /etc/kubelet.d directory. Kubelet will automatically pull the container image from NGC, and spawn a pod executing the container. The DOCA Firefly service will start executing right away.

View currently active pods, and their IDs (it might take up to 20 seconds for the pod to start)\ncrictl pods

View currently active containers, and their IDs\ncrictl ps

Examine logs of a given container\ncrictl logs

Examine kubelet logs, in case something didn't work as expected

journalctl -u kubelet \n

Please refer to the documentation for more information.

Documentation

The DOCA Argus Service programmer's guide is available DOCA Argus Service Guide.

License & EULA

This DOCA Microservice is licensed under the NVIDIA Networking Product Agreement.\nBy pulling and using this product, you accept the terms and conditions of this license.

Technical Support

Use the NVIDIA Developers forum for questions regarding this Software.

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

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Live machine introspection is performed at the hardware level, analyzing specific snippets of volatile host memory, to monitor threats in real time without impacting system performance. DOCA Argus does not violate privacy as information is extracted from kernel structures only.\n\nUnlike conventional tools, Argus runs independently of the host, requiring no agents, integration or reliance on host-based resources. This agentless, zero-overhead design enhances system efficiency and ensures resilient security in any compute environment, including bare-metal, virtualized, containerized and multi-tenant infrastructures. By operating outside the host, isolated, in its own trust domain, DOCA Argus remains invisible to attackers — even in the event of a system compromise.\n\nCybersecurity professionals can seamlessly integrate DOCA Argus with their SIEM, SOAR and XDR security platforms, enabling continuous monitoring, incident response, and automated threat mitigation while extending their existing cybersecurity capabilities into other environments (i.e., AI infrastructure).\n\nNVIDIA BlueField is a foundational security component for every AI factory and AI cloud, providing built-in, data-centric protection for AI workloads at scale. By combining BlueField’s acceleration capabilities with DOCA Argus’ proactive threat detection, cloud service providers and enterprises can secure AI factories without compromising performance or efficiency.\n\nA single BlueField card with DOCA Argus is to monitor an entire node.\n\n\n## Installation and Getting Started\nAll preparation steps are listed under DOCA's Container Deployment User Guide. Note: The DOCA Service container is configured for K8S-based deployment, hence the use of the docker pull command is discouraged.\n\nNote: The DOCA Service container is configured for K8S-based deployment, hence the use of the docker pull command is discouraged.\n\nPreparation steps for the DOCA Service As explained in the service's [DOCA Argus Documentation](https://docs.nvidia.com/doca/sdk/doca+argus+service+guide/index.html), there are some resource allocation steps needed before the service can be executed. It is recommended to go over the [Container Deployment User Guide](https://docs.nvidia.com/doca/sdk/nvidia+bluefield+container+deployment+guide/index.html) and follow the instructions that match the DPU on which the service will be deployed.\n\nAdjusting the .yaml configuration The .yaml configuration for our container is doca_argus.yaml.\n\nNote: The file is stored with the rest of the .yaml configurations as were pulled from NGC in the previous steps (See \"Installation and Getting Started\").\n\nThe main environment variables that should be adjusted according to the setup are the following:\n\nSERVICE_CONFIG_FILE - full service configuration file - Options are either a \"default\" config file or customized config file placed under /etc/argus or the yaml content string (presented by default in the downloaded yaml) Configuring telemtry FluentBit is integrated into the Argus service telemetry system to handle the export of telemetry data. This integration ensures that logs and metrics are efficiently collected and forwarded to the desired destinations for analysis and monitoring.\n\nThe integration from Argus service to FluentBit is locally, after that FluentBit can be configured to send the data to end destination or be a proxy for another FluentBit with additional security among them.\n\nCheck resources page for FluentBit configuration examples\n\nSpawning the container Simply copy the doca_argus.yaml file to the /etc/kubelet.d directory. Kubelet will automatically pull the container image from NGC, and spawn a pod executing the container. The DOCA Firefly service will start executing right away.\n\nView currently active pods, and their IDs (it might take up to 20 seconds for the pod to start)\ncrictl pods\n\nView currently active containers, and their IDs\ncrictl ps\n\nExamine logs of a given container\ncrictl logs\n\nExamine kubelet logs, in case something didn't work as expected\n~~~\njournalctl -u kubelet \n~~~\nPlease refer to the documentation for more information.\n\n## Documentation\nThe DOCA Argus Service programmer's guide is available [DOCA Argus Service Guide](https://docs.nvidia.com/doca/sdk/doca+argus+service+guide/index.html).\n\n## License & EULA\nThis DOCA Microservice is licensed under the [NVIDIA Networking Product Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-networking-products/). \nBy pulling and using this product, you accept the terms and conditions of this license.\n\n## Technical Support\nUse the [NVIDIA Developers forum](https://forums.developer.nvidia.com/c/infrastructure/doca) for questions regarding this Software.\n\n## Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","displayName","DOCA Argus",["D",1782973423564],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/doca/resources/doca_argus","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/Nvidia-Centric-DOCA.png","canGuestDownload","latestVersionSizeInBytes",39598,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[275],"displayLabels",[262,263],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_229":271,"_265":272,"_266":273,"_268":269},{"_229":264,"_265":264,"_266":267,"_268":269},"NSPECT-X1MD-DWQY","value","filter",{"_270":269,"_265":264},"category","label","field","DOCA","pltfm_doca",{"_270":274,"_265":272},"platform",{"_229":276,"_265":276,"_266":277,"_268":278},"NVIDIA AI Enterprise Supported",{"_270":269,"_265":276},"feature",{"_124":1453,"_126":1454},"ngcApps",[1406],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1387":1388,"_1389":1390,"_1391":1392,"_1393":1394,"_1395":290,"_155":314,"_1396":1397,"_1398":56,"_1399":103,"_1400":103,"_1401":1402,"_1403":103,"_1404":1405},"amplitude",{"_1384":1385,"_986":-7,"_1386":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1378":1379,"_1380":1381,"_1382":1383},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1261":198,"_1262":1263,"_1264":1265,"_1266":1267},"announcements",[],"consentState",{"_352":1251,"_1252":103,"_1253":31,"_1254":31,"_1255":1256},"consentConfig",{"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353},"showContactAdmin",1784219343490,[],[],[328,329,330,331,332,333],{"_45":60,"_47":71,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":119,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":59,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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