Artificial Intelligence in Security Feature
AI in the security setting is not a mystery. It’s not
designed to replace the human presence. Rather,
it’s designed to augment the human’s ability to
make accurate decisions more quickly in order to
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GIS and GPS data, health check and
diagnostics data… The list goes on. The
data generated from the management of
people, buildings and security systems is
This vast ocean of data tells a story
about what’s happening at a given point
in time, but the means by which the
human operator can correlate and
interpret that data in context is limited by
the AI tools provided by the developers
of the systems themselves. Currently,
these are fairly primitive in nature.
The most sophisticated form of AI is
the deep learning neural network
applied to video data sources. In the
security industry, convolutional neural
networks for image processing are used
extensively in automatic and live facial
recognition and object detection.
Automatic and live facial recognition
algorithms serve to solve very specific
problems around the authentication of
authorised persons or, indeed,
Object detection determines the
presence of objects and people with
defined characteristics such as a car
type, whether the individual is an adult
or a child and the type of clothing
they’re wearing. These technologies are
analysing the video sources in isolation.
A combination of AI technologies
could correlate the outputs of a number
of siloed AIs and paint a bigger picture.
This is happening gradually, but in most
cases it’s the human who makes the
choice to link the relevance of events.
These choices can be displayed in the
same interface which is a big step
forward when it comes to putting AI to
its best use.
The quality and format of the data
sources is the Achilles heel of achieving
highly reliable outputs.
Another subject attracting much
controversy in recent times is the
prevalence of biased data due to the
developers of global AI technologies
operating within an echo chamber.
Unrepresentative training data sets
can lead to a biased algorithm, thereby
rendering the results untrustworthy when
applied to a wider real-world scenario.
However, high-end providers have
invested heavily into balancing these
issues. In the National Institute of Science
and Technology reports, the better
algorithms are cited to be over 99%
accurate across a varied race and gender
demographic. This is encouraging.
In a risk situation, there’s no training
data if the AI is looking across multiple
data sources and correlating what may
appear to be unconnected events. This is
precisely why many AIs need a bedding-in
period to learn the environment. This
is perfectly normal. Only the bleeding
edge AI technologies can start
producing reliable results with minimal
labelled data, but their descriptive
language is at times beyond
comprehension, in turn making it very
difficult for the buyer to understand
what the AI actually does.
What might be described as a
resulting ensemble of AI technologies
could add more value than the
individual data source queries. This is
probably the most challenging sector for
AI but, when successfully implemented,
also brings the most rewarding results.
Use case scenario
The best use case scenario may be the
prevention of a terrorist incident due to
the correlation of relevant data across
multiple data sources (eg a blue car
parks in an unusual place on London’s
Bond Street at 12.00 pm, its type and
licence plate are recognised by the
system and an event created, aggregated
social media feed analysis picks up
boastful and threatening hate language
and an event is created, a known
terrorist wearing a red jumper walks
down Bond Street at the same time and
an event is created, an organised protest
is underway resulting in a congregation
of hundreds of people at the same time
which then means that space is an area
requiring increased surveillance).
Threats could come from anywhere.
Due to the combined use of several
types of AI, the location and possible
time of an attack could be averted by the
deployment of security officers to
disperse the crowd. With effective and
efficient police co-ordination, the
suspect can be detained for questioning
and a potential attack thwarted.
Without AI, correlating these
unconnected events would be a
somewhat slow and manual task. With
AI, however, the decision-makers can
act faster and more precisely.
In the security setting is not a
mystery. It’s not designed to replace the
human presence. Rather, it’s designed to
augment the human’s ability to make
accurate decisions more quickly in order
to avert disaster.
In today’s ever-evolving world, there’s
simply too much data available for a
human alone to analyse. Put simply, it’s
now time to embrace this technology
set. In doing so, new opportunities open
up as problems which are difficult to
solve – such as how we go about making
the COVID-19 world safer – can be
considered, thereby contributing
towards the safe recovery of the
economy without a high human cost.
Pauline Norstrom is CEO and Founder
of Anekanta Consulting