AI, hybrid architecture and the next generation of security technology

The past couple of years may have seen businesses juggle priorities as they weathered a highly changeable market, but security has remained one area of high importance. Companies still need to secure their assets — regardless of the macro trends — but how they are doing this is evolving.

Increasingly, businesses are delving further into intelligent technology to accomplish their security goals, drawing on artificial intelligence (AI), deep learning software, real-time analytics, hybrid cloud architecture and new infrastructure to support their security operations.

At the same time, new use cases are seeing technology that has historically been used primarily for security and surveillance purposes applied to very different areas, such as health. For example, one organisation we work with is using AI in conjunction with our network cameras to monitor and detect acute breathing patterns in individuals.

As we move into 2023, we can expect to see a greater number of use cases that draw on the full capabilities of modern security technology in novel ways. AI and deep learning software, now becoming more broadly available in the latest generations of security and surveillance technology, will play an increasingly important role in enabling these use cases.

Meanwhile, the rising popularity of hybrid cloud as an infrastructure model, the growing prevalence of edge computing and the emerging interest in Internet Protocol (IP)-based audio technology is breathing new capability into the security sector.

Here are five factors that are set to shape the security landscape in 2023.

An emerging convergence

There’s often a certain synchronicity in the way different technologies develop and are used together over time. This is certainly true for security technology. Already, next-generation hardware such as network surveillance cameras with built-in deep learning capabilities is being underpinned by hybrid cloud architectures to drive new functionality.

In the coming year, this convergence will only increase, as businesses see the value that a combination of technologies can offer. The convergence of AI, deep learning, next-generation hardware and hybrid architectures not only enables new use cases, it also leads to the enhancement of security, safety and operational capabilities.

AI for real-time action

There’s a lot of buzz around AI and machine learning technology, but that doesn’t mean it’s not warranted. The analytics capability of AI and machine learning systems gives us the ability to enable real-time responses to surveillance data.

However, the focus on the analytics aspect of AI and machine learning is likely to give way to a focus on the actionable insights it makes possible which, in turn, help organisations determine when and how to act in response to certain kinds of activity captured via network cameras and other sensor devices.

Insights at the edge

A key driver of analytics and the actionable insights it offers is data — the more, the better — and today’s network cameras produce the data needed to feed advanced analytics technology. However, for hardware that is dispersed over a large area or great distances, latency can be an issue that prevents real-time analytics and instant actionable insight.

For this reason, onboard AI and deep learning capabilities are growing in importance. When analysis is undertaken at the ‘edge’ of the network, in the network device itself, real-time actionable intelligence becomes possible in situations where seconds count.

Historically, analysis at the edge has been relatively restricted due to the computing power needed, but this is changing as more analytics functionality is built into security hardware itself, with demand for such capability increasing in the coming year.

Hybrid architectures

A lot of security solutions are built on legacy architecture, with recording and storage hardware typically sitting onsite. Despite improvements in communications infrastructure over the past decade, much of the video recording from surveillance cameras continues to be held in onboard storage, such as SD cards, and periodically offloaded elsewhere in batches.

Hybrid cloud architecture offers a way forward for those security networks that are largely founded upon legacy infrastructure, as it combines the on-premises nature of traditional surveillance cameras and the cloud-enabled future of IP-based network camera technology.

As organisations gradually modernise their security assets, we’ll see a greater uptake of hybrid infrastructure to support this evolution.

Smart sound

Just as IP-based network surveillance cameras have increasingly underpinned modern security infrastructure, IP-based network audio devices are also growing in popularity as a core part of the security technology mix.

From a security perspective, the growth of network audio, such as IP-based speakers, aligns with the prevalence and usage of network cameras in the local market. It may also, over time, reach further into the audio industry, bringing a new, connected element to that market, in addition to the security industry.

There is enormous opportunity for the security industry as these technologies converge, with greater capabilities arising as a result. At the same time, these factors lend themselves to new use cases outside the traditional security sector, something we’re likely to see more of in the coming year.

Image credit: iStock.com/seamartini