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NVIDIA’s cybersecurity partnerships combine telemetry and security processing from BlueField DPUs and DOCA with AI analytics and partner software for detection, exposure management, firewalling, segmentation, runtime protection, and policy enforcement. The integrations serve different environments—especially operational technology (OT) and industrial control systems (ICS), and enterprise AI-factory infrastructure—so they are not one product or a single shared partner roster.
How NVIDIA’s real-time monitoring architecture works
In NVIDIA’s OT/ICS architecture, BlueField is a programmable, out-of-band data processing unit (DPU) that can collect telemetry across hosts, networks, and applications. NVIDIA describes AI analysis of operational signals to identify anomalies and emerging threats, with findings informing policies that can be enforced at the edge. The company presents this as infrastructure-level visibility and enforcement combined with AI-driven analytics; it is an architectural description, not independent proof of efficacy or zero operational impact. NVIDIA’s OT and ICS cybersecurity overview
For AI infrastructure, NVIDIA describes BlueField and DOCA as sources of infrastructure-level telemetry and security processing. Partner platforms add functions such as network and host security, workload isolation, firewalling, runtime protection, and policy enforcement. Which functions are available depends on the specific integration and deployment.
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NVIDIA says DOCA Argus can monitor containers, virtual machines, and bare-metal systems without relying on host software agents or consuming host CPU resources. It analyzes telemetry for operational and security signals and compares runtime behavior with expected baselines. NVIDIA’s examples of suspicious activity include unauthorized process execution, shell activity, reverse shells, and runtime anomalies. NVIDIA Technical Blog on DOCA in-silicon security
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NVIDIA says Argus telemetry can be exported through tools such as Fluent Bit and Vector to existing security information and event management (SIEM), security orchestration, automation and response (SOAR), and extended detection and response (XDR) platforms, as well as enterprise data lakes. That can let organizations connect infrastructure signals to established security operations rather than treating the DPU as a standalone monitoring console. The actual data available and integrations supported should be confirmed for the specific deployment.
NVIDIA frames agentless visibility as useful in environments where installing host agents is impractical or downtime is unacceptable. That design goal does not establish that every deployment is non-disruptive; operational impact depends on implementation and the environment.
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Partner integrations differ by environment and date
NVIDIA’s announcements name different partners for different use cases. The examples below describe roles attributed by NVIDIA, not independent product tests or a claim that every partner is part of the same deployment.
OT, ICS, and critical infrastructure
On February 12, 2025, NVIDIA said Armis, Check Point, CrowdStrike, Deloitte, and World Wide Technology (WWT) were integrating its cybersecurity AI platform for critical infrastructure such as energy, utilities, and manufacturing. The post describes a platform involving BlueField-3 DPUs, DOCA, and Morpheus, with BlueField-3 acting as an embedded sensor that streams telemetry to Morpheus. It describes Armis Centrix as cyber exposure management and CrowdStrike Falcon as deployed on BlueField-3 DPUs for AI-powered threat detection and response. These are NVIDIA’s descriptions and partner statements. NVIDIA’s February 12, 2025 critical-infrastructure announcement
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NVIDIA’s OT/ICS overview also names Akamai, Forescout, Palo Alto Networks, Siemens, and Xage Security as partners integrating accelerated computing and AI into OT cybersecurity. A separate NVIDIA post about agentic AI and critical-infrastructure cybersecurity names Armis, Check Point, CrowdStrike, Deloitte, Forescout, Nozomi Networks, and WWT. These lists are tied to their respective contexts; they should not be read as a roster for one shared solution.
Enterprise AI Factory validated design
In a January 5, 2026 announcement, NVIDIA said nine partner platforms were integrated into its Enterprise AI Factory validated design. NVIDIA attributed the following roles to them:
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| Partner platform | Role NVIDIA describes |
|---|---|
| Armis Centrix | Continuous cyber exposure management |
| Check Point Infinity AI Cloud Protect | Network and host security using DOCA Argus telemetry |
| F5 BIG-IP Next | Kubernetes workload isolation and policy enforcement |
| Fortinet FortiGate VM | Firewalling and zero-trust segmentation |
| Palo Alto Networks Prisma AIRS | AI runtime protection |
| Rafay | Operations and multi-tenant isolation |
| Red Hat OpenShift | Application platform capabilities |
| Spectro Cloud PaletteAI and PaletteAI Secure | AI integration and management |
| Trend Micro Trend Vision One | Monitoring and policy enforcement |
The table reflects the roles NVIDIA assigned in its announcement, not a neutral evaluation or guarantee of feature availability in every configuration. NVIDIA’s January 5, 2026 Enterprise AI Factory announcement
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Start with the system you need to protect and the signals you need to see. A solution built around OT/ICS constraints may not address the same risks as an AI-factory data center, and an exposure-management platform does not serve the same role as a firewall or runtime-protection product.
- Environment: Is the target OT/ICS or critical infrastructure, an enterprise AI factory, or both?
- Coverage: Does the integration expose host integrity, network traffic, applications, AI workload runtime, or asset and exposure information?
- Detection and response: Does it provide anomaly or runtime detection, alerts, containment, firewalling, segmentation, or policy enforcement? Identify which actions are automated and which require an operator.
- Security operations fit: Can telemetry or findings reach the organization’s existing SIEM, SOAR, XDR platform, or data lake?
- Operational constraints: Can host agents be installed? Is downtime acceptable? Are controls permitted to enforce policies automatically in the target environment?
NVIDIA’s sources do not provide neutral comparative pricing, independent efficacy testing, customer outcome studies, or a head-to-head vendor ranking. Buyers should validate supported integrations, telemetry coverage, response behavior, and operational impact against their own requirements.
What NVIDIA’s performance figure does—and does not—show
NVIDIA’s cybersecurity solutions page claims “up to 600X faster” GPU-accelerated performance than CPU-only servers for AI inference and real-time monitoring across servers, packets, users, and machines. The page does not provide benchmark methodology alongside the claim, so it should be treated as NVIDIA’s stated capability, not an independently verified result. NVIDIA’s cybersecurity solutions page
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