A Look at AI

• By Florian Matusek
• Dec 11, 2025

Large language models (LLMs) have taken the world by storm. Within months of OpenAI launching its AI chatbot, ChatGPT, it amassed more than 100 million users, making it the fastest-growing consumer application in history.

Generative AI, or LLMs, are a type of artificial intelligence that can do everything from answering questions and explaining complex topics to drafting movie scripts and writing code.

Today, LLMs and other AI tools are creating new opportunities for greater automation across various tasks, including in the physical security industry. However, understanding their limitations and potential risks is essential.

Clarifying the Terminology
Let us start by defining terms often used when discussing these tools. Artificial intelligence, machine learning, generative AI — what are the differences? And how do large language models fit in?

Artificial intelligence. The concept of simulating human intelligence processes by machines. It refers to tools and methods that enable machines to learn from experience and adjust to new situations without explicit programming. Concisely, machine learning and deep learning both fall into the category of artificial intelligence.

Machine learning and deep learning. Artificial intelligence that can automatically learn with little human interference. Deep learning is a type of machine learning that uses copious amounts of data to train a system.

Generative AI. A type of deep learning that enables users to quickly generate content based on various inputs such as text and voice, resulting in multiple outputs in the form of images, video, and other types of security data.

Large language model (LLM). An artificial intelligence algorithm and type of generative AI that uses deep learning techniques and is fed massive amounts of information from the internet.

AI is a tool. Intelligent Automation (IA) is the result.

For most organizations, implementing AI comes down to two driving factors: achieving large-scale data analyses and higher levels of automation. At a time when everyone is talking about digital transformation, organizations want to use their physical security investments and data to ramp up productivity, improve operations, and reduce costs.

Automation is when specific tasks occur without people needing to do them. Once a process is set up in a program, it can repeat itself whenever needed, consistently producing the same result.

Traditional automation requires a clear definition from the start. Every aspect, from input to output, must be carefully planned and outlined by a person. Once defined, the automated process can be triggered to operate as intended.

Intelligent Automation (IA) allows machines to tackle simple or complex processes without these processes being explicitly defined. IA typically uses machine learning, like generative AI and natural language processing, to enable investigations using natural language search. Essentially, Artificial Intelligence is a tool that enables Intelligent Automation (the desired outcome). It empowers humans with the correct information at the right time and ensures they can focus on core activities instead of data patterns and analysis.

For instance, AI is becoming more democratized through various video analytics solutions. Retailers today can use directional flow or crossline detection analytics to track shoppers' behavior. Sports stadiums can automatically identify bottlenecks to ease foot traffic during intermissions. Organizations can also use people counting analytics to track occupancy levels to meet evolving safety regulations.

Physical Security Applications and Limitations
In the physical security industry, organizations commonly use machine learning and deep learning algorithms to detect patterns and classify data. The outcomes are based on probability, requiring human oversight to determine accuracy.

However, it is important to remember that AI solutions, including LLMs, are not always the best solution to a particular problem due to their heavy computational requirements. For example, for monitoring meeting room occupancy, a solution leveraging occupancy sensors can be more effective and efficient than a computer-hungry, AI-based camera system that uses computer vision to count the number of people in the room.

To ensure the most responsible and efficient use of AI and LLMs, involving humans in the decision-making process is essential. A human should have the final say in decisions made. Implement a robust system of reviewing, testing, and monitoring. User feedback systems can also provide valuable insights by allowing users to evaluate the algorithm's responses.

Overall Goals and Technical Feasibility
Despite the availability of out-of-box solutions, there are still myths about what AI can and cannot do. It is important to understand that most AI solutions in physical security are not one-size-fits-all, especially when solving specific organization problems.

Automating tasks or reaching a desired outcome is a process of determining technical feasibility. It involves identifying the existing solutions, technologies needed, compatibility issues, and other environmental factors. Even when feasibility is assessed, some organizations may question whether the investment justifies the outcome.

While AI is a key component to reaching higher levels of automation in the physical security industry, there is still much consideration, forethought, and planning required to achieve accurate results.

Maximizing AI in Security
One of the best ways to capitalize on new AI advances in physical security is by implementing an open security platform. Open architecture allows security professionals to explore AI applications that drive greater value. Leaders can try out new applications and select the ones that best fit their objectives and environment.

It is important to remember that AI is a means of achieving IA to optimize operations and enhance productivity. Responsible AI development and deployment are paramount. Partnering with organizations committed to data protection, privacy, and ethical AI use is crucial to maintaining cyber resilience and building trust.