2024  looks poised to be a breakout year for artificial intelligence (AI) in security. And it’s likely just the tip of the iceberg. 

In 2024 and beyond, security teams at all sized institutions will use AI technologies to enable the kind of security presence previously only possible with an enterprise-scale budget. Gartner Research anticipates that the use of AI in business will jump from 5 percent at the end of 2023 to more than 80 percent by 2026.

New AI tools will offer valuable insight into our security and operational environments, automate even more tasks so your staff can focus on core security objectives, and give rookie security officers the technologically-powered expertise to function like your most experienced veterans. Many tools already used by security teams fall under the AI umbrella. For example, video analytics tools for facial recognition or motion detection are quite sophisticated forms of AI. 

This article explores everything you need to know about AI trends in physical security in 2024 and beyond. We even begin with a quick primer to demystify some confusing AI-related acronyms we’re sure you’ve seen floating around. 


Essential AI terminology 

Artificial Intelligence sounds like an intimidating topic, but it’s actually quite straightforward. 

What is Artificial Intelligence?

What is AI  

AI is any software or computer algorithm that can complete what people consider an ‘intelligent’ task. It involves computer systems learning from data, identifying patterns, making decisions based on that information, and often, taking action or prompting human operators to take specific action. 

AI encompasses various technologies enabling computers to understand language, recognize objects, and make predictions or recommendations. From voice assistants like Siri to self-driving cars, AI powers many technological advancements we encounter daily, aiming to automate tasks, enhance efficiency, and solve complex problems.

What is Machine Learning?

What is Machine Learning (ML)?    

Machine learning is a specific type of AI that teaches computers to learn and make decisions from data without explicit programming—in other words, through trial and error instead of giving the software strict instructions. It works by feeding computers lots of information, allowing them to recognize patterns and make predictions or decisions based on that data. 

Imagine showing a computer many pictures of cats and dogs. Over time, it learns to tell them apart by identifying unique features in each, such as ears or fur patterns. This ability to learn from examples and improve with experience is what makes machine learning so powerful, helping computers make even more complex choices or predictions, like suggesting movies you might enjoy or detecting suspicious behavior of a person on a surveillance video feed. 

How does generative AI work?

What is Generative AI (GenAI)?    

If you heard about ChatGPT or AI art software in 2023, this is how those apps work. Generative AI is a type of artificial intelligence that creates new things like images, music, or text by learning patterns from thousands or, more often, millions of examples and then coming up with new things that look or sound similar. For example, showing an art GenAI program many images of flowers can create new ones that look like real flowers. 

What are Large Language Models

What are Large Language Models (LLMs)?     

Large language models are software systems that can understand human language. They've been trained on a lot of text from books, articles, and websites, so they know a huge amount of information. These models can help answer questions, write essays, translate languages, and more. 

They work by predicting what words or sentences might come next based on what they've learned from all the texts they've read. ChatGPT was a breakout success because, for the first time, its developer could figure out an effective way to train a GenAI on a large language model. 


2024 AI trends in physical security 

Here is what we see on the horizon for artificial intelligence in security. 


Massive proliferation of AI in surveillance 

In 2024, we'll see a rise in the use of Artificial Intelligence (AI) in surveillance systems. AI-powered video analytics systems will enable real-time threat detection and situation assessment, offering features like facial recognition, activity monitoring, and anomaly detection. 

By integrating intelligence and a degree of automated decision-making into conventional security measures, AI surveillance tools can instantly adapt and respond to potential threats. For instance, AI-enabled video surveillance systems can identify unusual patterns or behaviors, predict potential threats, and notify security personnel.

Learn More: [Definition] What is Physical Asset Surveillance?


Streamlined security data management 

Side-by-side with the rise of AI tools in coming years, we’re going to see a rise in Big Data techniques. Fortunately, managing the flow of large volumes of data is another area where AI can tackle busy work for security teams. 

The catchphrase ‘Big Data’ refers to collecting and analyzing large data sets that have only become possible with the rise of high-volume digital storage devices. Big Data analytics techniques are powerful but cumbersome. AI tools will aid human analysts in taming this information flow, detecting patterns, and creating actionable intelligence. 

We will also see machine learning aid in reporting. ML algorithms can monitor search queries, even repeated sorting and filtering activities, and automatically complete them for analysts.


AI-powered biometrics and access control 

When combined effectively, physical and digital security measures significantly minimize the threat of everyday attacks businesses encounter. The rapid advancement of genAI and the growing sophistication of deep fakes pose challenges even for seasoned security experts and their systems in identifying fake IDs and documents. 

These nearly flawless duplicates grant unauthorized entry through physical security barriers and eventually breach digital safeguards. Additionally, commonly used single-factor physical security access controls, such as proximity access cards, are now easily duplicated using inexpensive tools. 

In 2024, it becomes crucial for organizations to invest in protocols and technologies capable of detecting forged documents and verifying users' identities. By seamlessly integrating physical and digital security, organizations can protect their data amidst rapidly evolving threats.

Learn More: Electronic Access Control: How It Works & Why You Need It


AI accelerating IoT adoption and business system integration 

The Internet of Things (IoT) connects all kinds of different smart business systems, including access control systems, smart storage solutions, environmental controls, and inventory management software. This connectivity streamlines operations and enhances business teams’ overall efficiency. 

Some companies have stumbled finding ways to integrate traditional, legacy business systems with this latest generation of networked IoT tools. There has still been too much manual work needed to bridge that gap. AI software is poised to help those companies clear that last hurdle.


New analytics tools 

Security analytics are driven almost completely by AI technologies, and in 2024, we can expect to see even more advanced analytics applications appear in security systems. Advanced behavioral analytics currently in development will be able to identify changes in an individual’s actions from data culled from access logs and video feeds. This analysis will be pre-processed for your security officers, so the data is ready and waiting for them to review and determine threat responses. 

Other analytics tools in development will improve forensic responses. These tools can automatically search through log or video archives to analyze past behavior for notable events and automatically add them to case files.


LLMs used for social engineering attacks 

Soon, we anticipate a rise in attackers using AI tools—specifically LLMs—in more sophisticated and extensive ways to craft social engineering content. What will make headlines are the efforts to target global elections, particularly the 2024 U.S. presidential election, potentially causing political tensions worldwide. 

But on a more day-to-day level, organizations might also face challenges as AI-powered social engineering tactics become more difficult to track and identify. As a result, there will be a growing need for advanced detection capabilities to address these emerging threats from attackers looking to compromise access control credentials, passwords, and other sensitive business information. 


Attacks on physical hardware 

In 2024, powerful AI will be in the hands of the many, making sophisticated capabilities more accessible at scale to malicious actors. This will accelerate attacks not only in OS and application software but also—more concerningly for physical security professionals—across more complex layers of hardware and networked business infrastructure. 

Previously, bad actors needed to develop or hire very specialist skills to develop such exploits and code. Still, the growing use of genAI has started to remove many of these barriers. This democratization of advanced cyberattack techniques will lead to an increase in the proliferation of more advanced, more stealthy, or more destructive attacks. 


More ways to optimize work 

This key strength of artificial intelligence systems—their ability to process large volumes of data faster than people—will also help organizations deploy various resources more effectively. Solutions are already being developed for physical asset management or fleet management, where it’s best practice for vehicles or assets to follow a balanced or rotated use schedule. 

Fleet supervisors are quite familiar with the tendency for drivers to select a fleet’s newest vehicles. This can wreak havoc on maintenance schedules, but it takes a considerable amount of manual effort for a supervisor to track vehicle usage in a way that creates an effective rotation schedule. New fleet management dashboards could include an AI agent that could analyze usage against maintenance and depreciation schedules and rotate vehicle selections on the fly. 

Learn More: The Evolution of Fleet Management

In high-security settings where randomized guard tours are needed, AI tools may soon help balance optimal patrol routes with sufficient randomness. AI tools will further support many enterprise activities that we now measure using business intelligence techniques. Machine learning systems will have a place if an activity can be measured, analyzed, and optimized. 


AI will only become more powerful and pervasive in physical security 

AI’s future in security operations looks bright and will likely only get brighter. Video analytics is certainly an area that will continue to improve, with more advanced motion and object recognition becoming available. Advanced behavioral analysis tools are just now reaching the production stage. Some law enforcement agencies even piloted ‘predictive policing’ tools recently that helped them target high-risk areas for future crime. The initial results were positive enough that we will certainly hear more about this in the future.


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