DDN Advances Secure AI Factories with AI-Native Data Intelligence Infrastructure at NVIDIA GTC Taipei at COMPUTEX 2026

Company advances governance, security, and real-time control for enterprise-scale agentic AI environments

TAIPEI, Taiwan--(BUSINESS WIRE)--DDN, the global leader in AI and data intelligence solutions, today announced new advancements to its AI data intelligence platform designed to help enterprises deploy agentic AI faster, strengthen governance and security, reduce operational complexity, and maximize GPU efficiency across enterprise-scale AI factories. The innovations deliver real-time observability, policy-based control, secure multi-tenant isolation, and AI-native data orchestration optimized for large-scale training, inference, and autonomous AI workloads, helping organizations move AI initiatives from pilot to production with improved performance and ROI.

The announcement aligns with new AI infrastructure innovations being introduced at GTC Taipei, including NVIDIA Vera BlueField-4 STX architecture and NVIDIA DOCA security framework, designed to provide inline security, memory observability, and policy-based protection for AI-native storage and agentic AI workloads operating at enterprise scale.

As enterprises transition from experimental generative AI deployments to production-scale agentic AI systems, infrastructure requirements are rapidly evolving. Autonomous AI agents continuously retrieve, generate, reason over, and act on enterprise data in real time—creating new demands for governance, security, performance isolation, and operational efficiency across the AI data pipeline.

DDN powers some of the world’s largest AI factories, sovereign AI deployments, hyperscalers, and enterprise AI environments supporting millions of GPUs globally. The company's AI data intelligence platform, powered by NVIDIA accelerated computing and AI, helps organizations operationalize secure AI factories by combining high-performance data orchestration, governance, multi-tenant isolation, and real-time AI data services optimized for training, inference, vector databases, RAG pipelines, and autonomous AI environments. The platform is designed to align with NVIDIA DOCA’s agentless, inline security model while improving GPU utilization, reducing infrastructure bottlenecks, and accelerating enterprise AI deployment timelines.

“Enterprises are under enormous pressure to move AI from experimentation to production while controlling costs, governance risks, and operational complexity,” said Alex Bouzari, Co-Founder and CEO at DDN. “AI factories are becoming autonomous production environments where business outcomes depend on secure, real-time access to data. DDN helps organizations deploy agentic AI faster, maximize GPU efficiency, strengthen governance, and improve the economic return of their AI investments.”

“As enterprises move autonomous AI from pilot to production, a new class of secure, high-performance data infrastructure is essential to manage the massive, real-time demands of agentic workloads,” said Jason Hardy, vice president of storage technology, NVIDIA. “Combining NVIDIA Vera BlueField-4 STX architecture and NVIDIA DOCA security frameworks with DDN’s AI-native data intelligence platform enables enterprises to operationalize secure, scalable AI factories for training and inference at scale.”

NVIDIA Vera BlueField-4 STX architecture, featuring NVIDIA DOCA Argus, DOCA Vault, and DOCA Flow, introduces a modular AI-native storage framework designed to support the performance, scalability, memory observability, zero trust controls, and security requirements of enterprise AI factories. The platform combines accelerated computing, networking, and inline security enforcement to create a secure-by-design AI infrastructure for agentic AI environments.

DDN’s platform helps enterprises:

• Accelerate AI deployment from pilot to production through AI-optimized data orchestration for large-scale training and inference
• Improve application responsiveness and AI accuracy with low-latency infrastructure optimized for vector databases, RAG, and autonomous AI pipelines
• Secure multi-tenant AI environments with deterministic performance isolation and governance controls designed for enterprise and sovereign AI requirements
• Gain real-time visibility and policy enforcement across AI workflows to simplify governance, compliance, and operational management
• Maximize GPU utilization and infrastructure efficiency to reduce operational costs and improve AI ROI

The announcement also reflects a broader industry shift toward infrastructure-level AI security, where policy enforcement and protection operate directly within the AI data path rather than relying solely on traditional host-based defenses. NVIDIA DOCA frameworks introduced with Vera BlueField-4 STX enable inline enforcement, runtime visibility, inference protection, and AI-native data governance across distributed AI environments.

“Agentic AI fundamentally changes the operational and security requirements of enterprise infrastructure,” said Sven Oehme, CTO at DDN. “Organizations need infrastructure capable of governing and protecting AI data in real time without introducing performance bottlenecks. DDN’s platform is engineered to help customers scale secure AI factories efficiently while preserving ultra-low latency, maximizing GPU utilization, and reducing the operational burden of deploying enterprise AI at scale.”

For more information, visit ddn.com.

About DDN

DDN is the world’s leading AI and data intelligence company, powering the world’s most demanding AI workloads by keeping GPUs fed, efficient, and productive—at massive scale—so organizations can train, checkpoint, and infer faster with less footprint and power while achieving tremendous ROI from their AI investments. From hyperscalers and next-gen cloud builders to enterprises, governments, and research institutions, DDN delivers proven data intelligence at exabyte scale across millions of GPUs—so customers can deploy AI with confidence, accelerate time-to-value, and realize outsized returns. Discover more at ddn.com.

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