As evidenced by the many articles in Security Business just this year alone, AI development is happening at a breakneck pace – and not just for the security industry, but for the world at large. That said, these groundbreaking advancements in AI models have rapidly transformed security and video surveillance, introducing real-time situational awareness analytics that extends far beyond traditional rule-based analytics.
That has left security integrators and consultants in a quandary – and one that may feel quite familiar to those integrators and consultants who survived and remember the transition from analog to digital so many years ago. With this in mind, understanding AI capabilities and being able to properly communicate them to a customer’s IT and data analysis departments becomes critical to an integrator or consultant remaining a trusted source of technology.
If they can accomplish this feat, it will lead to increased business opportunities for both. For integrators, it will lead to expanded subscription-based as-a-service offerings, expanding their RMR. For consultants, it will position them as a trusted advisor who is continually increasing the value of an end-user’s existing investments in physical security technologies.
History Repeating Itself?
As AI capabilities keep advancing at a fast pace – faster than most integrators and security design consultants can keep up with – it brings up a troublesome aspect of the early years of physical security and IT convergence.
In the earlier days, the technical knowledge of customer and client IT personnel exceeded that of most integrators and consultants. The physical security industry developed a reputation for not knowing enough about the IT aspects of its own technology – some of which were subpar in ways that the industry’s sales and service people didn’t understand. It is critical that integrators and consultants do not allow this to happen when it comes to AI technology.
Today’s progressive security customers and clients employ data scientists and AI engineers whose AI technology knowledge far surpasses that of physical security industry folks. In much the same way that IT administrators would snicker at security software and technology sales and consulting efforts 15-20 years ago, these AI experts may very well be doing the same thing in today’s landscape.
There’s no escaping the fact that the knowledge landscape is changing so quickly that it is highly unlikely that physical security industry sales and consulting experts will be able to catch up; however, the good news is that closing that knowledge gap may not be as necessary as it was during the analog-to-digital transition.
The reason is that AI capabilities are far more proven than the clunky software efforts of ages past. Think about the degree of AI technology that would be needed to propel a self-driving vehicle – it requires hundreds of thousands of snap decisions by AI software in a minute or even a second. Now compare that to AI for video surveillance and the security use-case seems like child’s play.
This is a key aspect that didn’t exist during the digital transition. Security AI software and chip manufacturers are leaning on often open-source models that have enjoyed millions upon millions of dollars in investment and development. The security industry is simply reaping the rewards.
News stories frequently highlight AI-generated mistakes, raising concerns about its reliability; however, such risks primarily apply to general-purpose AI models and Large Language Model (LLM) generative AI used in business applications. In contrast, AI-driven security applications are narrowly focused, operating within predefined security policies and structured data environments, eliminating the possibility of hallucination or misleading responses.
It means that AI-enabled physical security applications are not prone to error, and they do not require personnel skilled in AI – just people skilled and knowledgeable in physical security. In fact, one of the key purposes of AI-enabled physical security system capabilities is to act as a significant force multiplier for existing security personnel.
While deep AI expertise isn’t necessary to use AI-enabled physical security applications, security industry professionals – from integrators to consultants to security directors – must understand how AI models function within their products. This knowledge is critical for explaining system reliability and effectiveness to AI-savvy customers and IT teams.
While security professionals don’t need to match the AI expertise of corporate AI engineers, they must be able to articulate how AI in their security solutions works, why it is reliable, and how it aligns with enterprise IT policies, including cybersecurity and data governance. This ensures IT approval for deployment within the corporate infrastructure and credibility when making security technology performance claims.
In the end, this means that history does not need to repeat itself. If integrators and consultants can simply explain the technology and what it can do, it will open the door to more business and trust in the technology to accomplish what it is designed to do.
Today’s emerging AI-driven surveillance systems integrate multiple advanced AI technologies, several of which directly contribute to physical security situational awareness:
Vision Transformers (ViTs): AI models designed for image and video analysis, enabling advanced object detection, activity recognition, and anomaly detection in security footage.
Large Language Models (LLMs): Text-processing AI trained on vast datasets, allowing security AI to interpret, summarize, and generate reports based on security alerts, incident logs, and policies.
Large Multimodal Models (LMMs): AI models that process multiple types of data (text, images, video, and audio), making them ideal for correlating security camera feeds, alarm data, and spoken alerts into a unified situational awareness framework.
Long Short-Term Memory Networks (LSTMs): AI models specialized in analyzing sequences of events over time, enabling security systems to track movement patterns, detect unusual activity durations, and identify anomalies based on deviations from normal behavioral timelines. LSTMs model event progression, helping to predict security violations before they occur.
Agentic AI: AI systems capable of autonomous decision-making and action execution based on predefined policies, reducing response time in security operations by dynamically adjusting surveillance priorities and responses.
Explainable AI (XAI): AI frameworks that provide transparent, human-understandable justifications for AI decisions, ensuring security personnel understand why alerts are triggered and how AI-generated recommendations align with security protocols.
All of these AI elements support Human in the Loop (HITL), ensuring that AI-driven security systems operate with human oversight where necessary; however, Agentic AI makes HITL essential, as AI is not just analyzing data but actively making decisions and taking actions based on security policies. This elevates the role of security personnel from constant monitoring to high-level decision-making, where AI assists rather than replaces human expertise.
At the same time, Explainable AI (XAI) makes HITL precisely effective by providing clear, human-understandable justifications for AI-generated alerts, recommendations, and actions. Instead of security personnel questioning why an AI system flagged an incident, XAI ensures they see the reasoning behind every decision, enabling faster validation and more informed responses.
By combining Agentic AI’s decision-making, XAI’s transparency, and HITL’s oversight, AI-powered security systems achieve true real-time situational awareness and act as a powerful force multiplier for security operations personnel, allowing them to process vast amounts of real-time security data, make faster, data-driven decisions, and reduce response time. The key is that they can trust AI-driven recommendations, knowing they are policy-based and explainable.
