The integration offloads network security telemetry and Zero Trust policy enforcement directly to the NVIDIA BlueField data processing unit (DPU) silicon leveraging NVIDIA DOCA™. Decoupling security from host compute enables line-rate microsegmentation with no CPU and GPU performance overhead on AI training and inference workloads.
Key takeaways
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Zero performance overhead
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The joint solution offloads network security telemetry and policy enforcement directly to NVIDIA BlueField DPU silicon, preserving 100% of host CPU and GPU compute for AI workloads.
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Hardware-isolated defense
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Running security within dedicated DPU hardware keeps controls isolated and immune to host-level vulnerabilities or compromised host operating systems.
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Protection for autonomous agentic AI
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Hardware-enforced boundaries prevent compromised autonomous coding and software agents from executing unauthorized commands or moving laterally across the data center fabric.
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Line-rate data path enforcement
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Zero Trust microsegmentation is applied at wire speed across high-speed AI network fabrics without degrading token throughput or inflating cost per token.
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Comprehensive agentless AI coverage
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Delivers unified Zero Trust policy management across GPU training clusters, real-time inference nodes, petascale storage, and HPC model registries without installing software agents on host machines.
Frequently Asked Questions (FAQ)
Frequently Asked Questions (FAQ)
The solution anchors host-level sandboxes to hardware-enforced boundaries directly on the BlueField DPU. If an autonomous agent is compromised via prompt injection, memory poisoning, or tool manipulation, the hardware-level microsegmentation prevents it from executing unauthorized commands or moving laterally across the data center.
No, the solution extends agentless microsegmentation directly through the DPU silicon. This enables organizations to protect sensitive AI training pipelines, context memory, petascale storage, and model registries without placing software agents on host compute nodes.
By running security controls on dedicated DPU hardware, the security processor remains entirely isolated from the host operating system. This architecture ensures security policies remain tamper-resistant and operational even if the host environment is compromised by host-level vulnerabilities.
The integration secures core components of an AI factory environment, including GPU training clusters, real-time inference nodes, agentic AI systems, petascale storage (vector databases and training datasets), and HPC model registries.
The solution continually identifies assets and enforces automated policy updates. This approach allows security teams to map dependencies, facilitate rapid threat containment, and automate policy adjustments as AI systems evolve.
Yes, policy enforcement and traffic inspection occur directly within network fabrics at wire speed, ensuring maximum data path throughput during enforcement, without introducing latency.