Akamai and Nvidia expand AI factory security collaboration with Zero Trust integration

Akamai Nvidia Partnership Expands to Secure AI Infrastructure

Akamai Technologies, a $23.3 billion cybersecurity and cloud services provider, announced an expanded collaboration with Nvidia to integrate advanced security capabilities directly into AI infrastructure systems 1

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. The Akamai Nvidia partnership builds on an architecture agreement the two companies introduced in February 2026, now bringing Akamai Guardicore Segmentation to the Nvidia Vera BlueField-4 STX storage architecture, powered by the Nvidia DOCA software platform 2

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. This integration aims to layer Zero Trust architecture into AI factories themselves, protecting the data, context memory, and autonomous agents that increasingly run modern enterprises.

Addressing LLM-Driven Cyber Threats Through Advanced Segmentation

The combined solution enables AI factory operators to enforce workload-aware segmentation, monitor agent behavior, and implement threat containment at the infrastructure layer without taxing the GPU, CPU, or storage cycles that AI workloads depend on 2

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. "AI factories are becoming critical assets that must be designed for containment, especially as frontier LLM-driven attacks increase the speed and scale of cyber threats," said Ofer Wolf, Senior Vice President of Enterprise Security at Akamai 1

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. The architecture operates on the principle that intelligence must precede enforcement, with Akamai Guardicore Segmentation continuously mapping how workloads, applications, and data interact across hybrid environments including data centers, cloud infrastructure, Kubernetes clusters, and edge systems.

How Zero Trust Architecture Protects Agentic AI Factories

The AI factory security integration establishes identity-based Zero Trust as a property of the infrastructure rather than an additional product. Security policies are defined by workload identity, application context, and runtime behavior instead of static network addresses 1

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. Nvidia Vera BlueField-4 STX, programmable through Nvidia DOCA, applies these security policies in the data path at line speed, inside the infrastructure fabric rather than on the host 2

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. This approach enables accelerated computing to continue unimpeded while maintaining robust protection. Kevin Deierling, Senior Vice President of Networking at Nvidia, emphasized that "data is the foundation of agentic AI factories, powering the intelligence behind autonomous decision-making and making robust protection more critical than ever for enterprises" 1

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Securing AI Infrastructure Without Performance Trade-offs

The expanded integration removes the traditional trade-off between security and performance in securing AI infrastructure. Akamai Guardicore Segmentation provides the intelligence layer, with visibility extending across the full lifecycle of AI workloads, surfacing abnormal patterns and unauthorized access to sensitive data 2

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. When a workload is compromised, the blast radius is limited to a small, identified segment of the environment while the rest of the AI factory continues operating uninterrupted. Security functions including segmentation, telemetry, anomaly detection, and isolation of compromised systems run inside the infrastructure fabric, operating at the speed of accelerated computing. This agentless architecture observes AI workloads, including training pipelines, inference services, data ingestion systems, and orchestration platforms, without interfering with them.

Timeline and Broader Context for AI Security Deployment

Akamai Guardicore Segmentation integrated with Nvidia BlueField and Nvidia DOCA is expected to be available in the second half of 2026 for implementing workload-aware segmentation in AI factories. The integration with Nvidia Vera BlueField-4 STX is expected to be available on storage and infrastructure partner platforms in the first half of 2027 1

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. The timing aligns with Akamai's recent $3.5 billion convertible note offering, which included $1.75 billion of 0.00% Convertible Senior Notes due in both 2030 and 2032, with funds earmarked for AI-related infrastructure investments 1

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. The company has also reported AI bot traffic on its network surging over 300% year over year, underscoring the growing importance of securing AI infrastructure as enterprises increasingly rely on autonomous agents and agentic AI systems for critical operations.