What happens when business-critical applications are accessible but do not work reliably? Or when a compromised device on the network communicates with other systems undetected? For businesses, this can quickly lead to lost productivity, operational disruption and security risks. Secure AI Networking therefore brings together two tasks that go hand in hand: intelligent network operations and a security architecture that tackles threats where connections are established. This article shows how Secure AI Networking strengthens network availability, security and control.

Today, networks are expected to do much more than simply transport data from point A to point B as quickly as possible. Applications are distributed across on-premises data centers and cloud environments, employees access them from various locations, and an ever-increasing number of connected devices are expanding the infrastructure. At the same time, AI is accelerating the search for vulnerabilities—on the part of both defenders and attackers. The time it takes to identify risks and respond to them is thus becoming a decisive factor.

This raises a strategic question for corporate leadership: How can an increasingly complex infrastructure become a reliable foundation for the business—without itself becoming a risk factor?

AI-Native Networking: More than just a Smart Assistant

Our networking and security partner, HPE, is pursuing an approach with Secure AI-Native Networking that combines AI-powered operations, a comprehensive network portfolio, and integrated security. To this end, the technologies and expertise of HPE Aruba Networking and HPE Juniper Networking are being consolidated into HPE Networking, with the goal of optimizing availability and security and making the growing complexity manageable.

This involves both using AI to improve the network and building high-performance networks for AI applications. AI is thus viewed not merely as an additional user interface, but as an integral part of analysis, optimization, and automation. This significantly reduces the operational burden on your network, reveals interdependencies across the entire infrastructure, and enables targeted action.

From Troubleshooting to Predictive Operations

When a video conference stutters, the first assumption is often: the network is to blame. But the cause could just as easily lie with a cloud service. HPE addressed this very example at this year’s InfoGuard Security Lounge — watch the recording here: Instead of chasing down assumptions, IT teams should be able to identify where the problem actually originates.

This is where AIOps—AI-powered IT operations — comes in. Network and connection data are continuously analyzed, anomalies are correlated, and potential causes are narrowed down. This allows problems to be proactively identified, concrete recommendations for action to be derived, and specific corrections to be automatically implemented after appropriate approval. The focus extends beyond individual components to the quality of the connection from the end device to the application.

However, a self-learning network is not a free pass for uncontrolled AI interventions. On the path to self-managing networks, AIOps therefore distinguishes between insights, recommendations, assisted actions, and automated problem resolution. When implementing this, you should define — based on your specific needs — which changes are automatically permitted, when approval is required, and how interventions are reviewed.

Security belongs within the Network—not at its Perimeter

Stable operation alone, however, is not enough. Even a network that functions flawlessly from a technical standpoint can transmit unwanted communication. Zero-trust is also central to an AI-native approach. Users, devices, and applications are not automatically trusted simply because they are located within the corporate network. Access is verified and restricted to the necessary permissions. Centrally managed policies should consistently enforce this protection across locations, data centers, and cloud environments.

This becomes particularly evident with connected devices: If an IoT device that has been unremarkable until now suddenly establishes unusual connections or transmits significantly more data than usual, this is cause for investigation. AI-powered device profiles and behavioral analyses can highlight such anomalies and provide information for downstream access controls.

However, the security architecture must also be able to handle threats for which no established signature yet exists. To this end, traditional protection mechanisms must be supplemented with AI- and machine learning-based methods. In the case of SRX firewalls, for example, this includes AI-Predictive Threat Prevention, which is designed to detect and defend against new and advanced malware. Additionally, insights into threats should be translated into effective protective measures as quickly as possible.

The network thus becomes not only a transport path but also a location where security-related anomalies can be detected and protective measures can be enforced across the board.

Your Benefits: Availability, Efficiency, and Control

These capabilities give rise to three key objectives for network modernization:

Strengthen business continuity: Detect disruptions earlier, isolate causes more quickly, and limit the spread of attacks through appropriate access rules.
Reduce operational overhead: Automate recurring analyses and appropriate routine tasks so that specialists have more time for challenging tasks.
Enable controlled growth: Build new locations, applications, and AI projects on an infrastructure where performance and security are developed in tandem.

When making investment decisions, we therefore recommend looking beyond a simple comparison of features and acquisition costs. Measurable goals are more meaningful: How long does root cause analysis take? How many manual interventions are required? Which business-critical connections are monitored? And how consistently can security policies be enforced?

“The business case isn’t created by the ‘AI’ label, but by demonstrable operational improvements.”

InfoGuard Delivers HPE Networking Expertise with Holistic Cybersecurity

As a leading HPE networking partner, we combine networking expertise with a comprehensive understanding of cybersecurity. Our experienced and certified experts support companies from architecture and design through engineering and integration to operations — on-premises, in the cloud, or as a managed service. We take a holistic approach to networking and security: We combine the planning and implementation of modern networks with zero-trust concepts, managed services, and our cyber defense expertise. This transforms the introduction of individual technologies into a comprehensive evolution of the entire security and operational architecture.

This holistic expertise is also recognized externally: In the “ISG Provider Lens Cybersecurity Report 2026”, InfoGuard was named a Leader a total of seven times in Switzerland and Germany.

Our goal is clear: Your network should keep pace with your company’s needs and help you securely take advantage of new opportunities.

Beat Sommerhalder and Michael Woduschegg from HPE Networkingdemonstrated howAI- -basednetwork operations, and integrated security complement oneanother at the InfoGuard Security Lounge in June 2026. In the presentation “From Complexity to Control—AI-Native Network Security”, learn about the role the network plays in effective cyber resilience.