What Is edge AI and why does it matter in security-sensitive contexts?

What happens when a device collects sensitive data and sends it to a cloud infrastructure located far away—perhaps even in another country?

Every data transfer introduces latency, dependence on network connectivity and, in some cases, a non-negligible risk to data security and privacy.

There are highly sensitive contexts where data security, governance and privacy protection are non-negotiable. In these situations, cloud-based AI—the approach currently adopted for most artificial intelligence applications—may not always be the most suitable solution, making it necessary to evaluate so-called edge architectures.

Today, the global edge AI market is estimated to be worth $25.65 billion and is expected to reach around $143.06 billion by 2034, with a compound annual growth rate (CAGR) of 21.04%.

But what exactly is edge AI? How does it differ from other AI deployment models, and when is it the right approach?

Edge AI combines artificial intelligence systems with edge computing—an approach that enables data to be processed directly at the source, or as close as possible to where it is generated—reducing latency and increasing control over data.

This means that data can be collected and analyzed directly on:

• Industrial sensors
• Surveillance cameras
• Wearable medical devices
• Smartphones and smartwatches
• Internet of Things (IoT) devices

Local gateways and on-premise servers close to the network

This is the key difference from cloud AI, where data is collected at the source and then transmitted to remote cloud infrastructure to be processed, analyzed or stored.

Today, cloud computing remains the most widely used approach to data management, as it is well suited to many applications—especially those requiring high computational power, such as deep neural networks and advanced natural language processing systems.

In these cases, the substantial computing power and storage capacity offered by major cloud providers are essential.

However, there are scenarios where edge AI is emerging as the preferred option, particularly when data security and privacy protection are critical.

Edge AI is often associated with higher levels of security because raw data can remain within the local environment, reducing exposure during transmission to remote infrastructures.

Edge AI offers several benefits that become strategic in specific contexts.

First, processing data locally and in real time allows faster decision-making, dramatically reducing response times.

Consider, for example, an intensive care unit where patients are connected to devices that continuously monitor their vital signs. An edge AI system integrated into these devices could detect anomalies instantly and generate immediate alerts, allowing medical staff to intervene more quickly.

Edge AI can also provide economic advantages. Since data is analyzed locally, the system follows a very different model compared to non-edge environments, where each data processing request typically requires a call to an external service that charges based on usage.

Another important aspect is that edge devices can operate even without internet connectivity, ensuring service continuity and reducing dependence on centralized servers.

From a security perspective, this approach offers key advantages for protecting sensitive data, including:

• Reduced cybersecurity risks: Since data remains local and is not transmitted externally, the risk of interception during transmission or exposure to cyberattacks is significantly reduced.
• Data sovereignty and regulatory compliance: When data is processed and stored within local infrastructures, organizations retain stronger legal and operational control over it. This can simplify compliance with regulations such as the General Data Protection Regulation (GDPR) and strengthen data governance by clearly defining who can access and manage the information.

Edge AI in action: four use cases in regulated sectors

The growth of the edge AI market is largely driven by its applicability in sectors such as healthcare, public administration, smart cities and transportation.

The following are some concrete examples of how edge AI can be applied.

Clinical assistants for medical consultation transcription

Edge AI can be used in medical practices to automatically transcribe and summarize patient consultations.

An AI system installed on the physician’s computer can analyze the conversation with the patient in real time and generate a summary along with a preliminary version of the clinical record.

Since the processing happens directly on the local device, sensitive healthcare data does not need to be sent to external cloud infrastructures, reducing transmission risks and facilitating compliance with data protection regulations such as GDPR.

Autonomous driving and real-time decision making

Another example comes from the transportation sector, particularly autonomous driving systems.

Vehicles equipped with sensors and cameras must continuously analyze large volumes of data to identify obstacles, pedestrians or changes in traffic conditions.

With edge AI, this information can be processed directly on the vehicle’s hardware, reducing latency and ensuring operational continuity even when network connectivity is unavailable.

Multilingual customer support through self-service kiosks

Self-service information kiosks are increasingly common in airports, shopping malls and theme parks.

Visitors can ask questions in their own language and receive directions or information about services, shops or routes within the facility.

By running natural language processing models directly on the device, edge AI enables near-instant responses, regardless of network availability and without relying on remote cloud systems.

Voice assistants for field operators

Edge AI can also be extremely useful in environments such as construction sites, industrial plants or remote infrastructures—like railway construction zones or offshore platforms—where network connectivity may be limited or unstable.

In these contexts, voice assistants installed on local devices can help field operators work more efficiently. Workers can query technical manuals, complete checklists or verify safety procedures using voice commands, without depending on cloud infrastructure.

In these situations, edge AI offers multiple benefits. Beyond improving operational efficiency, it reduces the risks associated with managing and transmitting sensitive data.

There are many other contexts where this technology can prove valuable, including manufacturing, office environments and even private homes.

To address increasingly diverse application needs, the Velvet family by Almawave includes, alongside large-scale language models, lighter solutions such as Velvet 2B and Velvet Speech 2B, designed to operate effectively in edge environments.

These models are optimized to run on lightweight infrastructures, making them suitable for applications that must operate in environments with limited computational resources.

Velvet 2B enables the implementation of AI capabilities in contexts where computing power and connectivity are constrained, while Velvet Speech 2B supports dynamic voice interactions with advanced speech processing capabilities.

By enabling local execution, these models can help reduce latency and keep data within the organization, a critical factor in regulated sectors such as healthcare and public administration.

Interested in learning more about our AI models designed for edge deployment?