Fortinet and Arista Networks have moved to formalize a joint approach to securing and operating AI-focused data center infrastructure, unveiling a validated Secure AI Data Center solution that has already been deployed by semiconductor company Monolithic Power Systems (MPS).
The announcement reflects growing demand among enterprises for architectures that can support high-density AI workloads while addressing security, performance, and operational complexity at the same time.
As organizations race to deploy large-scale AI training and inference environments, traditional data center designs are struggling to keep pace. AI workloads introduce extreme east-west traffic, ultra-low latency requirements, and unprecedented volumes of encrypted data, all of which place pressure on both networking and security layers. Fortinet’s Secure AI Data Center solution, developed in close collaboration with Arista Networks, is positioned as a reference architecture that aims to address those challenges through a modular, multivendor design.
The solution builds on Fortinet’s existing Secure AI Data Center framework, expanding it through tighter integration with Arista’s high-performance networking platforms. Rather than relying on a single-vendor stack, the architecture is designed around best-of-breed components that can scale independently as AI infrastructure evolves. According to Fortinet, this approach is intended to reduce vendor lock-in while giving operators a validated blueprint for deploying AI environments with predictable performance and security characteristics.
At the core of the design is a zero-trust security model that extends from the network core through AI model training and inference. Fortinet contributes its ASIC-accelerated firewalls, encrypted traffic inspection, segmentation, and automated threat response, while Arista provides ultra-low-latency networking optimized for large AI and machine learning clusters. The integration is intended to secure data pipelines, storage, and GPU clusters without introducing bottlenecks that could degrade model performance.
One of the technical differentiators highlighted by Fortinet is the offloading of HTTPS and TLS processing to its purpose-built security ASICs. By moving cryptographic workloads away from server CPUs, the architecture frees up compute resources for AI tasks such as forward passes and token generation. Fortinet claims this can deliver up to 33 times higher performance for encrypted traffic inspection, with sub-microsecond latency, reducing jitter and improving consistency under heavy load. For AI operators, that translates into higher tokens-per-second throughput and tighter tail latencies during training and inference.
AI Infrastructure Deployment Challenges
The modular nature of the architecture is also designed to accommodate rapid changes in AI hardware. As new generations of GPUs and accelerators enter the market, organizations can integrate them without redesigning the entire data center. Zero-touch provisioning and automation are built into the reference design, with Fortinet estimating deployment times can be reduced by as much as 80 percent compared with more manual approaches.
Monolithic Power Systems, which designs and manufactures power management solutions for AI, cloud, and automotive markets, served as an early deployment partner for the joint solution. MPS worked with Fortinet to design and validate an AI data center capable of supporting high-density GPU clusters across its global operations. According to the company, the integrated networking and security stack provides the visibility and control required to protect sensitive models and data while sustaining the performance levels demanded by AI workloads.
Industry-wide, the challenges facing AI infrastructure deployments are becoming more visible. Infrastructure costs continue to rise, while shortages of specialized skills and increasing architectural complexity have slowed many projects. Fortinet points to research suggesting that the majority of AI deployments fail to reach production scale, often due to a combination of performance bottlenecks, security gaps, and operational friction. The company positions its Secure AI Data Center architecture as a way to address those risks by converging security and networking into a unified operational model.
The partnership also underscores a broader trend in the data center market toward tighter collaboration between networking and security vendors. As AI traffic patterns diverge from traditional enterprise workloads, standalone security layers can struggle to keep up without impacting performance. By embedding security functions directly into the data center fabric and aligning them with high-speed networking, vendors are attempting to balance protection with throughput.
For Arista Networks, the collaboration aligns with its focus on data-driven networking for cloud and AI environments. The company’s switching platforms and cluster load balancing technologies are widely used in hyperscale data centers, and the partnership with Fortinet extends those capabilities into security-sensitive AI deployments. Both companies emphasize that the solution is designed to be open and extensible, allowing customers to integrate additional tools and platforms as needed.
While the Secure AI Data Center solution is being promoted as a validated blueprint rather than a fixed product, its deployment at MPS provides a concrete example of how enterprises can operationalize AI at scale. As more organizations move from experimentation to production AI systems, reference architectures that address security, performance, and manageability simultaneously are likely to play a growing role in infrastructure decisions.
The announcement reflects an industry recognition that AI infrastructure cannot be treated as a simple extension of existing data center designs. Instead, it requires purpose-built architectures that account for new threat models, extreme performance requirements, and rapid hardware evolution. Fortinet and Arista’s joint effort represents one attempt to provide that foundation in a way that balances flexibility with operational certainty.
Executive Insights FAQ
Why are AI data centers creating new security challenges for enterprises?
AI workloads generate massive encrypted traffic, rely on sensitive training data and models, and require low-latency east-west communication, increasing both attack surface and operational risk.
What differentiates a Secure AI Data Center from traditional data center designs?
It integrates zero-trust security, high-performance networking, and automation specifically optimized for AI training and inference rather than general-purpose workloads.
How does hardware-accelerated security affect AI performance?
Offloading cryptographic and inspection tasks from CPUs to dedicated ASICs preserves compute resources for AI processing and reduces latency and jitter.
Why is a multivendor reference architecture important for AI infrastructure?
It allows organizations to avoid vendor lock-in, adopt new accelerators more easily, and optimize each layer of the stack independently.
What role do early deployments like MPS play in validating AI architectures?
They demonstrate real-world performance, scalability, and operational viability, helping other enterprises assess risks before adopting similar designs.
