AI data sovereignty has become one of the most important topics in governance for regulators, governments, healthcare providers, financial institutions, and critical infrastructure providers. As consensus builds around what AI sovereignty means, organizations must figure out how to maintain and monitor it across today’s highly distributed infrastructures.

Like all governance challenges, demonstrating compliance requires real-time monitoring and predictive analytics, both of which start with complete, real-time visibility. Here’s a high-level look at how the role of visibility changes with AI and what needs to happen to extend sovereignty to AI without starting from scratch.

What AI data sovereignty means

Maintaining sovereignty over AI means being able to see, control, and demonstrate: 

• How data moves across networks and international borders
• Where and how AI data gets processed and stored
• Who can access data
• Compliance with individual countries’ laws (which might overlap or even differ) 

The challenge begins with maintaining sovereignty as models train and grows even more complex as organizations evolve to AI inference and self-directed agentic workflows. 

How AI impacts sovereignty challenges 

Sovereignty still requires securing data and maintaining compliance across large distributed multi-cloud environments, but AI adds scale and complexity. Where traditional applications follow predictable workflows and store data in known locations, AI applications may pull from multiple repositories, sending prompts and transferring data between public and private clouds. They also may use APIs that route data internationally and generate new artifacts that must be governed.

These practices create visibility gaps that in turn lead to sovereignty risks as companies struggle to document:

• What data AI systems are accessing
• When and how regulated data is crossing international borders
• Whether AI providers are processing data in approved jurisdictions

Sovereignty challenges turn into network monitoring challenges

The rigorous demands of AI – faster speeds and response times, data volumes, cloud security – bring the underlying network front and center. To maintain and demonstrate sovereignty, organizations must be able to see, control, and document the flow of data across hybrid multi-cloud network environments.

AI infrastructures intensify monitoring challenges

Where traditional monitoring emphasizes user-to-server traffic and predominately north-south traffic, AI workloads generate massive volumes of east-west traffic inside data centers. Network monitoring, packet visibility, flow analytics, and AI visibility all must work together to deliver the telemetry needed to document handling of AI data in highly dynamic environments.

Network visibility helps close the sovereignty gaps

Data sovereignty creates the rules, network visibility generates proof of compliance. Network visibility enables the AI-scale monitoring needed to demonstrate that sovereignty controls are in place and working. For example, that AI data gets processed and stored within a particular state, country, or region.

Network visibility goes hand-in-hand with AI visibility

Like “sovereignty,” the term “AI observability” is becoming an industry hot button. This term generally refers to tracking:

• Model performance: Accuracy, response rates and quality, hallucinations, latency, cost per query
• Input and data: Data quality issues, drift 
• Model behavior: Bias, toxic responses, consistency
• Infrastructure: GPU utilization, memory, API performance, availability and uptime

This information plays a role in AI governance and sovereignty but doesn’t tell you anything about the health and security posture of the underlying networks. AI-scale network visibility is also needed to monitor and demonstrate critical aspects of model utilization and how data gets moved and stored.

Visibility across AI models and data includes identifying:

• AI application traffic and shadow AI
• Which AI models employees are using
• What data is being shared with AI systems
• When AI applications can accessing what data
• Prompts that contain regulated information

Sovereignty and storage issues include:

• Which cloud regions receive requests
• Destination of traffic

• Which services are being used
• Whether workloads are communicating with unauthorized destinations
• Cross-border transfers
• Potential policy violations

Enterprises invest in network performance monitoring (NPM) solutions from Keysight alliance partners like ExtraHop, LiveAction, and Riverbed to help ensure the safe, efficient handling of AI data. The network telemetry delivered by their solutions — enabled by visibility data from Keysight — becomes an important part of the audit trail that documents things like:

• Where and how network latency may be impacting performance  
• Cloud utilization   
• Third-party AI usage

What to look for in a visibility architecture as AI inference comes of age

Sovereignty requirements — and requirements to get the maximum value from their investments in AI — will drive investments in modernizing and scaling network visibility and monitoring architectures. IT and security leaders should keep key criteria in mind as they evaluate new solutions or investments: