Your enterprise WAN isn’t ready for AI workloads. Here’s why

Artificial Intelligence (AI) is becoming a core business capability, and with that shift comes a major architectural reckoning. AI has exposed weaknesses that were easy to overlook in the pre-LLM world. However, AI isn’t putting stress on the network just because it’s “bigger”. It’s because it’s less predictable. So, the race to implement AI is forcing enterprise IT leaders to redesign the global enterprise WAN.

According to IDC, 94% of enterprises say their networks limit their ability to run large data/AI projects in some way. So, the pressure is on to build AI-ready network infrastructure that’s adaptable, reliable, and quick to deploy.

What is the impact of AI on enterprise network infrastructure?

Traditional traffic patterns were relatively stable and predictable because they were optimized for human traffic. But AI doesn’t behave as humans do.

AI introduces volatility through:

• Latency sensitivity becomes unavoidable: Inference traffic reacts poorly to jitter or unexpected delay. Bandwidth alone cannot compensate for unpredictable paths or variable underlay performance.
• Traffic becomes elastic and often chaotic: Model updates, cloud-to-cloud transfers, east-west flows, and sudden bursts all place pressure on network intelligence. Older QoS methods, designed around fixed classes and static rules, fail quickly once AI traffic enters production.
• Compute shifts outward, not inward: Warehouses, retail sites, manufacturing floors, distribution hubs are all locations that act as micro data centers running inference locally. That shift demands stable performance in regions where connectivity may be uneven or unpredictable.

Greg Duffy, Product Marketing Director at CATO Networks commented:

“Organizations must now choose where and how to deploy AI, balancing cloud agility with on-premises control. Many land somewhere in the middle, which increases design complexity. Across all options, one expectation is the same: the network must support rapid change, seamless access, and a consistent user experience.”

Where are enterprises struggling with AI and their networks?

As AI deployments accelerate, enterprises are discovering that their network architecture and configuration isn’t flexible enough to accommodate AI workloads.

They face two clear pressure points when creating AI-ready networks:

1. Networks can’t handle AI-driven performance demands: Routing based solely on link status is no longer sufficient. Decisions must consider real-time latency, jitter, and packet loss. Throughput is increasingly shaped by delay, not speed. Many networks cannot adapt fast enough.
2. Managing new forms of risk: AI introduces threats like model data exposure, prompt leakage, shadow AI across departments, LLM-driven competitor intelligence risks and faster reconnaissance by attackers. IT teams need visibility into what employees type into AI tools and where that information travels.

To address the network performance, security risk, IT leaders need more than incremental tweaks. They need to design WANs that are adaptive, intelligent and secure at a global scal

Why does AI traffic need increased network performance?

AI traffic presents characteristics that are unfamiliar to many legacy networks that impact performance:

• Elephant flows during model distribution
• Microbursts during inference
  Sharp volume swings driven by prompts, updates, and new workflows

Beyond a point, adding bandwidth capacity doesn’t add much benefit if latency remains high or routing is inflexible. Throughput depends on both speed and delay, and delay dominates above relatively moderate bandwidth levels.

According to CATO Networks’ Greg Duffy:

“That’s why global underlay matters. No AI system performs well on top of inconsistent transport.”

What new security threats does AI expose enterprises to?

Expereo’s CIO, Jean-Philippe Avelange summarized the AI security challenge clearly:

“AI is unforgiving. On security, AI is speeding up both attackers and defenders, so the risk of doing nothing is often higher than the risk of automating. And in my experience, boards now expect AI-driven insights. If your network can’t provide normalized telemetry across regions and suppliers, your AI program will hit a wall.”

AI breaks the old perimeter and introduces risks that most enterprise security stacks cannot fully interpret, including:

• Prompt-level leakage: Employees may unintentionally share internal plans, customer data, or strategic details with external LLMs.
• Shadow AI: Staff often experiment with tools outside the approved ecosystem, creating unknown exposure points.
• Governance gaps: Regulation is evolving quickly. Organizations need policy frameworks that can adapt just as quickly.
• AI-specific security attacks: Prompt injection, model poisoning, and other techniques require new layers of defense.

Additionally, employees, workloads, and data now move freely across cloud, SaaS, edge, and remote environments. Static hardware boundaries cannot follow because:

• Enterprise traffic is increasingly dynamic.
• Security needs to follow identity and workload, not geography.
• Teams need a single environment rather than a stack of disconnected appliances.

Expereo’s Matthew Lea provided a potential example of how AI can introduce security risks:

“Imagine a Customer Service Manager asks an LLM to summarize the Q4 2025 NPS scores for sentiment. The data has been ingested by the model and has the potential to use it for any future inference or training.

Next, a junior salesman at a competitor asks the LLM “How would you go about targeting Company X?” And the LLM responds, “Well here’s a list of all the customers as of Q4 2025 and these are the tip 10 most unhappy and their sentiment was XYZ. Would you like me to draft a targeted email to the most relevant contact at each organization?” The consequences of this scenario would be dire from a compliance, commercial and reputational perspective.”

AI forces convergence of networking, security, and data governance, whether teams are ready for it or not.

So what can enterprises do to secure the network for AI workloads?

The answer to ensuring the security of AI workloads is Secure Access Service Edge (SASE). SASE offers a cloud-delivered framework that brings networking and security together under shared context. Essentially this enables AI-driven operations without compromising on performance.

A unified SASE solution delivers:

• Consistent policy enforcement: Appling security rules across users, devices, and workloads, globally, in real time.
• Predictable performance: Traffic inspection and routing happen without introducing the latency spikes that can disrupt inference or model distribution.
• Global visibility: Continuous telemetry ensures IT teams see where AI workloads travel and how they behave. Crucial for governance and compliance.

As CATO Networks’ Greg Duffy states, “Security can’t operate in isolation. AI workloads need protection without adding latency. SASE gives you a structure where both can co-exist.”

Implementing SASE means more than adding a security layer. It allows IT teams the ability to balance, speed, control, and protection across distributed environments.

How do you develop an AI-ready network architecture?

Supporting AI workloads isn’t about tuning what you already have. It requires a deliberate architectural shift to treat performance and security as a single system.

To do so, enterprises need to rethink both the underlay and the overlay.

An AI-ready underlay must deliver:

• Normalized performance across regions: Latency, jitter, and packet loss must be measured and managed consistently, regardless of provider(s).
• Intelligent path selection: Routing decisions must account for real-time conditions, not static link status.
• Supplier automation: Provisioning, monitoring, and remediation need to scale globally without manual intervention.

On top of a strong network foundation, SASE can be implemented as the control plane for AI workloads by enabling:

• Programmability: Policies adapt dynamically as AI traffic patterns change.
• Elasticity: Inference spikes and model updates scale without manual reconfiguration.
• Distributed intelligence: Security inspection and policy enforcement move closer to users and workloads.
• Zero Trust: Identity-driven protection follows users, devices, and AI workloads wherever they operate.
• Full telemetry: Continuous, end-to-end visibility across users, clouds, edges, and regions.
• AIOps: Predictive analytics and automated remediation replace reactive troubleshooting.

Together, underlay and overlay form a single adaptive system that’s capable of supporting AI workloads without sacrificing performance or security.

However, it’s important to remember that AI-ready architecture is hard to design and to operate. SASE environments should adapt continuously as AI traffic patterns shift, models evolve, and regulatory expectations change across regions. Without a managed approach, many enterprises find themselves trading infrastructure complexity for operational overload.

Are you struggling to build AI-ready network infrastructure?

Organizations that adapt quickly to the new needs of network traffic and security will deliver smoother experiences, stronger protection, and faster paths to business value. Those that delay may find their networks become the main barrier to AI adoption.

For support in redesigning your enterprise WAN for next-gen AI workloads with Managed SASE, get in touch with Expereo today.

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