GLOBAL ELECTRONIC SECURITY FORUM
Vol. 1, No. 7 JULY 2025
Page 22
By Jay Jason Bartlett, Cozaint Corp.
The accelerated development of artificial intelligence (AI) is causing
a significant shift in the physical security scene. Security systems are
now using AI to anticipate threats, comprehend complicated actions,
and enable a genuinely proactive defense, going beyond simple
recording and reactive monitoring. Predictive video analytics, a crucial
AI application at the vanguard of this revolution, is radically altering
the way we safeguard infrastructure, people, and assets.
The Dawn of Proactive Security: What’s Happening Today
For decades, video surveillance was largely a forensic tool –useful
for investigating incidents after they occurred. The sheer volume of
video data made real-time human monitoring impractical and prone
to error. AI has flipped this paradigm on its head.
Today, AI-powered video analytics are performing a multitude of in-
telligent tasks that vastly improve situational awareness and response
capabilities.
Moving beyond simple motion detection, current AI systems can
identify behaviors that deviate from the norm. This includes detecting
loitering in restricted areas, unusual crowd movements, or the pres-
ence of abandoned objects. These systems are continuously learning
what “normal” looks like in any given environment, allowing them to
flag suspicious activities for immediate human review.
These applications, while impressive, are just the beginning. They
represent the foundational shift from reactive surveillance to intelli-
gent, real-time threat identification. The true power of AI in physical
security lies in its ability to predict.
The Rise of Predictive Video Analytics: Beyond the Horizon
The transition from reactive to proactive is already in motion, but
the next wave of innovation will fully embrace AI’s predictive capabili-
ties. This is where it will move beyond its current capacity of detecting
what is happening now into anticipating what might happen next.
Just around the corner, we can expect to see functionality that
includes:
Proactive Threat Forecasting
By analyzing vast datasets of historical incidents, environmental fac-
tors (e.g., time of day, weather, local events), and behavioral patterns,
AI will be able to predict potential security risks before they fully
materialize. For example, the system might identify an increasing like-
lihood of a breach in a specific area based on subtle changes in usual
activity, allowing security teams to deploy resources preventatively.
Contextual Decision-Making
Future AI systems will integrate data from a wider array of IoT de-
vices, sensors, and even external data feeds to provide a more holistic
understanding of a situation. For instance, a system might analyze
crowd density in conjunction with public transport schedules and
local event calendars to predict potential choke points or areas prone
to disorder, enabling preemptive crowd management.
Agentic AI for Automated Response
While human oversight will remain crucial, agentic AI will empower
systems to initiate automated responses based on predefined rules
and verified threats. This could include locking down doors in a spe-
cific zone, triggering localized alarms, notifying emergency services,
or even deploying autonomous robots for initial assessment – all
without direct human intervention in the immediate moments of an
incident. This dramatically reduces response times and human error.
Explainable AI” (XAI) for Enhanced Trust and Auditability
As AI systems become more complex and autonomous, the need
for transparency increases. XAI will provide insights into why an AI
system flagged a particular event or recommended a certain action,
building trust and allowing security professionals to understand and
validate the AI’s decisions for audit and training purposes.
Multi-Modal Fusion for Comprehensive Intelligence
The integration of video analytics with other sensor data – such
as audio analytics (detecting gunshots, breaking glass, aggressive
voices), lidar, radar, and access control data – will create a truly uni-
fied security ecosystem. This multi-modal fusion will provide a much
richer and more accurate understanding of events, enabling more
informed and effective responses.
Self-Learning and Adaptive Systems
AI systems will become even more sophisticated in their ability to
learn and adapt over time, continuously improving their detection
and prediction capabilities based on new data and evolving threat
landscapes. This self-optimization will lead to increasingly resilient
and effective security postures.
The Role of the Human Element
It’s important to stress that the development of AI in physical secu-
rity enhances rather than diminishes the function of human security
experts.AI frees up human operators to concentrate on more compli-
cated threat assessment, incident response, and higher-level strategic
decision-making by taking over the time-consuming, repetitive activi-
ties of ongoing monitoring and early anomaly identification.
No machine can replace the critical judgment, ethical thought, and
adaptability in which the human element offers. Security teams can
be more effective, precise, and eventually proactive in protecting our
physical world thanks to AI, which acts as a potent
force multiplier. Debunking the underlying fear
which pits humans against artificial intelligence,
the future of physical security will actually involve
a strong, mutually beneficial partnership between
the two.
Jay Jason Bartlett is the Managing Editor of Security.
World and the CEO of Cozaint Corporation, a
manufacturer of security surveillance solutions. Jay
has over 40 years in the high-tech industry and over 15
years in physical security. visit: cozaint.com
The Rise of Predictive Video Analytics:
Moving Beyond Reactive Surveillance