Page 12 - Security Today, November/December 2024

C O V E R S T O R Y
Gaining a
Competitive Edge
AI is now a surveillance reality, but deploying it at the edge, in
the cloud or hybrid is an individual organizational decision
By Ramy Ayad
their future technology
Ask most companies about
plans and the answers
will most likely include
AI. Then ask how they
plan to deploy it, and that is where the re-
sponses may start to vary.
Every company has unique surveillance
requirements that are based on market fo-
cus, scale, scope, risk tolerance, geographic
area and, of course, budget. Those factors
all play a role in deciding how to confi gure
a surveillance system, and how to effec-
tively implement technologies like AI.
At the Edge
One method that has been widely em-
braced by the industry is Edge AI.
The emergence of edge computing has
transformed the way security and surveil-
lance data is gathered, managed, processed
and stored for effi cient use. Instead of relying
solely on centralized data centers in a virtu-
alized cloud environment, edge computing
involves processing data closer to the source,
reducing latency and alleviating bandwidth
constraints, among many other benefi ts.
Rapid advancements in edge computing
intelligence and capabilities have also paral-
leled 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.
Depending on a company’s preferred
model, some organizations will prefer to
minimize network bandwidth and lower
total costs by confi guring an edge-based
surveillance system. Others prefer a cen-
tralized cloud-based operation. Still others
may opt for a hybrid approach, giving them
the best of both worlds. In fact, many com-
panies, including Hanwha, view this as an
1 2 ideal ecosystem where edge and cloud solu-
tions co-exist, with neither approach being
“better,” and instead simply offering differ-
ent sets of features and benefi ts.
Enter Edge AI
Occurring simultaneously with the accel-
eration of edge computing have been ad-
vances in deep learning (DL), a subset of
AI. This trend has made it easier to bring
AI capabilities on-board cameras, simply
another way of saying it’s possible to en-
able more instances of AI locally, and ef-
fectively, at the edge.
Edge AI refers to running AI models
on devices at the “edge” of a network, such
as surveillance cameras, rather than man-
aging them centrally using remote servers
or in the cloud. With Edge AI, more func-
tions 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 their own pace. Edge AI can also
be a pathway to a hybrid on-prem/cloud
alternative. In an Edge AI model, the ma-
jority of data is stored in the cloud, while
only the most often used data is distribut-
ed over a network to the user’s fi ngertips.
This signifi cantly reduces bandwidth and
storage requirements.
The key benefi ts of Edge AI include
lower long-term operating costs by avoid-
ing ongoing cloud service fees, as well as
increased security and privacy benefi ts
by keeping sensitive data processing on-
premises and on the local network.
AI on a New Level
Another trend to consider also falls under
the broader defi nition of edge computing:
the Internet of Things (IoT), which is the
entire universe of mobile and connected
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