Aaron Saks, Director of Product Training at Hanwha Vision America discusses the multiple benefits of edge AI for security.

The benefits of edge AI

Nearly every organization has modernization at the top of their priority lists, but not every company has the resources to transform itself overnight.

The surveillance industry is no exception.

The emergence of technologies like AI is reshaping how surveillance systems are configured and deployed, leaving many security professionals to choose the path that makes the most sense for their long-term success.

One method widely embraced by the industry is edge AI, which many companies are using as an effective method for the way security and surveillance data is gathered, managed, processed and stored for efficient use.

Edge computing involves processing data closer to the source, with the potential to reduce latency and alleviate bandwidth constraints, among many other benefits.

Edge AI refers to running AI models on devices at the “edge” of a network, such as surveillance cameras, allowing centralized management of more functions that are carried out on the camera itself.

Edge AI can be a long-term strategy on its own, enabling a company to migrate to AI at its own pace.

Edge AI can allow for easier deployment, as a company’s existing camera infrastructure can be used without the need to replace every camera all at once or invest in expensive servers.

The users still benefit from full AI functionality and performance without the potentially burdensome maintenance costs associated with total camera replacement or video management system (VMS) licensing – while at the same time getting the most use of their current technology expenditures.

It’s a similar transition strategy to what the industry saw with the migration from analog to IP, when many companies used encoders to “convert” existing cameras, ultimately getting more lifespan out of their current infrastructure without having to do a full upgrade all at once.

Edge AI can also be an efficient cloud alternative.

In an edge AI model, even though the majority of data is stored in the cloud, only the most frequently used data is distributed over a network to the user’s fingertips.

This significantly reduces storage requirements for individual devices.

The key benefits of edge AI also include lower long-term operating costs by avoiding ongoing cloud service fees, as well as increased security and privacy benefits by keeping sensitive data processing on-premises and on the local network.

Rapid advancements in edge computing intelligence and capabilities have also paralleled the maturation of cloud technologies, to the point where edge computing is now capable of performing tasks that, in the past, only the cloud could handle.

It’s all about options. Some organizations will prefer to minimize network bandwidth and lower total costs by configuring an edge-based surveillance system.

Others prefer a centralized cloud-based operation. Still others may opt for a hybrid approach, giving them the best of both worlds.

In fact, many companies view this as an ideal ecosystem where edge and cloud solutions co-exist, with neither approach being “better,” and instead simply offering different sets of features and benefits.

Securing the edge

Of course, once any degree of network connectivity or shared access is attached to an edge device, the potential for intrusion or vulnerability exists.

This leads to a heightened need for securing the devices on the edge of a network, at the point where a company’s internet service comes onto the network at each endpoint before the traffic reaches a centrally orchestrated network.

Security at the edge can be highly effective and the fact that it’s decentralized gives organizations more options for managing their own unique security requirements.

Securing the edge of an organization’s network computing also protects data and workloads in remote locations, which can be more vulnerable to threats and intrusions.

Many customers running AI models on the edge will attempt to build a wall of security around their devices, placing cameras in an isolated network that doesn’t have internet access.

That way, they’re not as vulnerable to attacks. In this case, it is critical when you choose an Internet of Things (IoT) device or IP cameras to have many layers of protection against attacks.

Edge security can also be configured in a layered approach, based on the idea that the more “walls” that potential bad actors have to penetrate, the harder it is to ultimately reach the device.

An edge AI approach can benefit large enterprises with existing infrastructures of hundreds of cameras as well as smaller organizations just starting to adopt AI.

Edge AI offers a flexible, cost-effective and phased approach for organizations to incrementally adopt and deploy AI capabilities in their video surveillance and monitoring systems.

There are also more solutions available to make the logistics of performing edge AI more cost-effective and efficient.

There are devices designed to support AI apps targeted toward different verticals.

Users who have those applications can pick and choose new AI detections, functionalities and analytics to load into a device on a camera-by-camera basis.

And it’s still done at the edge, allowing them to leverage their existing infrastructure.

When to consider cloud…

From a hardware perspective, if a company is satisfied with its current hardware infrastructure and doesn’t plan to upgrade anytime soon, edge-based AI can be an ideal model.

However, any hardware you buy is limited to what it can process.

Of course, there will be improvements on the model and potentially new detections added, but it can’t compare to the cloud’s resources and ability to deliver immediate updates.

It’s important to weigh the pros and cons based on organizational needs.

Again, hybrid solutions provide a great deal of efficiency since users can run most of the needed AI models on the edge and only pay subscriptions for high-processing AI models.

Looking ahead

Companies today face increased security threats and they are managing their operations with fewer resources and tighter budgets.

They need options when choosing the types of solutions that will work best for them – and that solution may be found on the edge, in the cloud or a hybrid combination of both.

This article was originally published in the special February Influencers Edition of Security Journal Americas. To read your FREE digital edition, click here.