[Tech Guide] Every AP Should Become a Security Sensor

Wireless: The Foundation of Enterprise Networks Reduce blind spots and attack surfaces by embedding security into the network

위드네트웍스 성영삼 상무

[DataNet] Wireless has become the foundation of enterprise networks, yet corporate network security remains largely centered on wired infrastructure. While internet gateways are tightly controlled, wireless networks—where the largest number of devices are connected—are often left without the same level of security oversight. By embedding security into every AP, enterprises can maintain a significant level of network security without having to build separate security infrastructure. This article examines the current security challenges facing enterprise wireless networks and explores potential solutions.

The Security Gap in the Era of AI-Powered Wireless Networking

The busiest entry point for devices in an enterprise network is no longer the wired port—it is wireless. From laptops and smartphones to IP cameras and sensors, most newly connected devices in the workplace now connect through wireless networks. Yet the focus of security investment remains centered on boundaries designed for the wired era. Internet gateways and servers are protected by layers of security controls, while the busiest part of the network—the wireless segment—is largely treated as an authentication layer that simply asks whether a device should be allowed to connect. There are few mechanisms in place to monitor what connected devices actually do.

As wireless becomes the default network with Wi-Fi 7 and AI-powered networking, this security gap in wireless networks becomes a security gap across the entire enterprise. This article examines the structural security gaps emerging behind the transformation of the WLAN market and explores potential solutions.

The WLAN Market Is Being Reshaped by AI Networking

The enterprise WLAN market has entered a clear growth phase. According to IDC analysis, Wi-Fi 7 revenue in the first quarter of 2026 increased more than fourfold year over year, accounting for 44.5% of managed AP revenue. Dell’Oro Group has identified 2026 as the year enterprise Wi-Fi 7 becomes mainstream and forecasts that Wi-Fi 7 will account for more than 90% of indoor AP revenue by 2028. In Korea, the transition to Wi-Fi 7 is also underway, ranging from telecom operators’ internal networks and public Wi-Fi on city buses to school wireless networks.

What is noteworthy is that this generational shift is not simply about competing on speed. HPE completed its acquisition of Juniper and has positioned Mist-based “self-driving networks” at the forefront of its strategy. Cisco has introduced “AgenticOps” based on AI agents, while Extreme Networks has launched an operations platform with agentic AI built in.

The fact that the U.S. Department of Justice required HPE, as a condition of approving its Juniper acquisition, to divest its Instant On wireless LAN business and license Mist’s AI operations (AIOps) source code to competitors demonstrates that AI operations software is being recognized as a key strategic asset in WLAN competition, alongside hardware. Gartner has also forecast that by 2026, 30% of enterprises will automate more than half of their network operations. Yet one item is conspicuously absent from this evolution: security.

Operations Have Evolved, but Security Was Left Behind

The security gap in WLANs is not a flaw in individual products but a structural problem rooted in network architecture. Most enterprise security devices are positioned at internet gateways and around server segments, but much of the communication and lateral movement between devices connected to the same wireless network does not pass through these boundaries. Attacks such as Evil Twin and wireless DoS occur over the air, where wired security devices cannot see them in the first place. Likewise, with Rogue APs, determining which devices they are attracting and what those devices are doing requires visibility into the wireless environment.

An analysis by Nozomi Networks of more than 500,000 wireless networks worldwide found that only 6% had adequate protection against deauthentication attacks. According to Palo Alto Networks’ 2025 report, 32.5% of devices connected to enterprise networks are outside the control of IT departments.

This is not merely a theoretical risk. In the “Nearest Neighbor” attack disclosed in 2024, the Russian hacking group APT28 first compromised an organization located across the street after finding that the target organization’s internet services were protected by multi-factor authentication (MFA). The attackers then remotely accessed the target’s corporate Wi-Fi through a PC inside the compromised organization. The internet perimeter was tightly secured, but the organization’s wireless network effectively became a backdoor accessible with nothing more than an ID and password. Even with protocol-level enhancements such as Protected Management Frames (802.11w PMF), attack techniques designed to bypass these protections continue to be reported, suggesting that protocol-level defenses alone are not sufficient.

This does not mean there have been no countermeasures. Wireless Intrusion Prevention Systems (WIPS) are a proven security measure, to the extent that the National Information Security Basic Guidelines require applicable organizations to deploy WIPS for wireless LANs. However, traditional WIPS architectures, which rely on dedicated sensors and separate management servers, require additional investment in infrastructure and operational resources. Because monitoring is primarily focused on anomalies at the wireless layer, it can also be difficult to understand what a device is doing inside the network within the same context. Even when threats are detected, alert fatigue remains a significant challenge: in a 2025 SANS survey, 73% of organizations identified false positives as the biggest challenge in threat detection.

Three Requirements for Wireless Security

Three key requirements have emerged in the market to close this security gap.

First, detection must be moved closer to the edge. An AP is both a point that directly receives wireless frames over the air and the first point through which decrypted device traffic passes before being sent over the wired network. Structurally, the AP is therefore the only location that can directly receive wireless signals over the air while also correlating them with decrypted traffic at the same physical point.

When APs become sensors, the service coverage area and security monitoring range are aligned, significantly reducing the spatial blind spots that can occur with overlay architectures. Monitoring also remains continuous as devices move through the network via roaming. Because the detection and blocking points are aligned, threats can be blocked or isolated immediately at the point where they are detected. The resulting savings in the cost of building and operating a separate sensor network are an additional economic benefit.

Second, threat determination must become more intelligent. The fundamental problem is that individual events often appear harmless on their own. A few port connections, periodic external communications, or a single large data upload could each be normal business activity. A threat becomes apparent when these events are linked into a chain of behaviors progressing from reconnaissance to covert communication and data exfiltration. This is also why frameworks such as MITRE ATT&CK describe attacks as chains of activities rather than as individual signatures.

Rather than simply filtering out alerts to reduce their volume, it is therefore important to automatically triage events by connecting them within context, assessing the likelihood of an actual attack, and leaving only the events that require human attention. In public-sector and financial environments, where audits and incident investigations follow, an explainable report should be generated alongside the determination, documenting which signals were observed and in what sequence led to the conclusion. Because the definition of normal behavior varies by environment, the determination criteria must also be adaptable to the density and behavioral patterns of each site for false-positive reduction to be sustainable. Gartner’s identification of AI-driven restructuring of security operations centers (SOCs) as a major cybersecurity trend for 2026 reflects the same direction.

Third, the entire process must be capable of operating on-premises. AI networking from major global vendors is often delivered through cloud-based management platforms. However, even though regulations have become more flexible with the introduction of the National Network Security Framework (N2SF) in 2025, configurations that send security events to external clouds remain difficult to accept in Korea’s public-sector, defense, and financial environments. A structure in which detection, analysis, and intelligence are completed entirely within the customer’s network, without external dependencies, is not simply an option in the Korean market—it is a prerequisite for adoption.

A Structure Where Every AP Becomes a Sensor

One example that integrates these three requirements is WithNetworks’ integrated wireless management and security solution, AiriX, and its wireless security function, HawkAI.

AiriX is built around a simple principle: rather than adding more security devices, turn the APs that are already installed into security sensors. AiriX Wi-Fi 7 APs incorporate on-device IDS sensors, enabling them to detect 36 types of threats in real time at the point of access without requiring separate sensors or appliances. These threats span both the wireless layer—including Rogue APs, Evil Twin attacks, and deauthentication attacks—and the network layer, including DDoS, port scanning, C2 beacons, DNS tunneling, and data exfiltration. Detected events are collected and centrally managed through an APC management server configured as a high-availability (HA) cluster. This integrated architecture, connecting on-site AP detection, centralized monitoring, and on-premises AI-based threat determination, is based on AiriX’s patented technology.

Detection is followed by determination. When the APC identifies circumstances that suggest a potential attack among the collected events, the HawkAI analysis server performs an in-depth analysis of detailed logs from the relevant AP. An on-premises LLM cross-validates multiple attack signals observed by each AP and determines whether an actual attack is taking place based on the behavioral context leading from reconnaissance and covert communications to data exfiltration. It also automatically generates a report containing the rationale behind the determination, enabling administrators to immediately decide on the appropriate response. For threats that can be addressed through security policies, response actions such as blocking or isolating the device can be executed, connecting detection, determination, and response within a single system.

Intelligence used for identifying countries and applications is also supplied through a proprietary offline database. This eliminates dependence on external cloud services and commercial data throughout the entire process, from detection through AI analysis, allowing the solution to be deployed in closed or segmented network environments as well.

Major global vendors also offer AP-based wireless intrusion detection and cloud-based AI operations capabilities. HawkAI’s differentiation lies in combining four elements—AP-based threat monitoring, centralized management, LLM-based AI analysis, and offline intelligence—into a single on-premises operating architecture without dependence on external cloud services. Rather than continuing to deploy and operate WLAN infrastructure and wireless security as separate systems, it provides a practical alternative in which security is embedded directly into the network infrastructure itself.

The Criteria for Selecting a WLAN Are Changing

The speed of Wi-Fi 7 will eventually become standardized across the market, and AI-powered network operations are also moving in the same direction as global vendors compete to develop similar capabilities. The remaining question is simple: Can these faster wireless networks protect themselves?

As long as the door through which the largest number of devices enter and exit the network remains the least monitored, the evolution of AI networking will remain incomplete. It is time to add new questions to the criteria for selecting a WLAN: What can this network detect? How does it determine whether something is a threat? And where does that determination take place?

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