The risks and rewards of AI in physical security

Organizations want to use their physical security data to enhance safety and improve operations. Leaders are taking a closer look at how artificial intelligence (AI) and security intersect, producing tools like intelligent automation and intelligent search that can help them achieve new security outcomes.

AI is on the rise in physical security

The 2026 State of Physical Security Report offers more insights about AI adoption. Did you know that 46% of end users working in the procurement, management, or use of physical security technology plan to deploy AI in the next five years? In fact, 21% of end users aim to integrate AI into their security operations in 2026, with the most common goals being to automate event triggering, emergency response, and repetitive tasks.

Though vendors are releasing new AI models and AI-enabled analytics solutions, decision-makers need to remain vigilant about the risks and limitations of AI. It’s also important to consider compliance with regulatory frameworks that govern the responsible development and use of AI applications.

Want to learn how AI security is evolving and what it means to choose solutions built with responsible AI practices? This blog has it all.

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What’s the difference between AI and intelligent automation?

When we talk about AI in security, it’s important to clarify what we mean.

AI refers to tools and processes that enable machines to learn from data and adjust to new situations without explicit programming. It encompasses a wide variety of concepts and techniques, including machine learning and deep learning.

Intelligent automation, on the other hand, combines AI with other rules, actions, and intuitive UX to create solutions to real-world problems. By merging AI with automation, intelligent automation can bridge the gap between advanced technology and delivering practical outcomes. This way, humans stay at the forefront, with intuitive functionalities designed to augment user capabilities.

What does this boil down to? Where AI is the tool, intelligent automation is the human-centered solution.

How is artificial intelligence used in physical security?

Today, the physical security industry has a more grounded understanding of what AI can do. Many know that AI isn’t perfect, but they’re still curious about how the technology is advancing.

Below are a few examples of how AI and security are coming together:

Making sense of all the data

The volume of video and data collected by physical security systems continues to grow. This can make it challenging for operators to process and act on information effectively. AI-enabled applications can provide new insights from this data to help improve efficiency and safety. The result? Enhanced problem-solving and better decision-making.

AI can help organizations achieve various goals. For example, it can detect threats more quickly and then automate responses, such as building evacuation procedures. Retailers can use AI to better understand customer behavior. Other organizations might use it to streamline parking or track occupancy levels. AI-enabled tools like directional flow and people counting analytics use data to help identify bottlenecks, all while ensuring compliance with safety regulations.

Enhancing video investigations

Using intelligent automation-enabled search tools, operators can identify and investigate suspicious activity to reconstruct event timelines in minutes. These tools can also help security teams query specific information not available in traditional reports, such as “Who accessed the office after hours?” or “Who has been entering restricted areas?” This can help isolate suspicious cardholder activity, pinpoint potential insider threats, or simply paint a more accurate picture of operations.

Natural language search makes it easier to process large amounts of data. Teams can now search for specific people, vehicles, or even colors. This speeds up investigations and makes them more accurate. Intelligent automation-powered algorithms can quickly sift through video recordings, helping to isolate specific details—locating all footage featuring a red vehicle within a given timeframe, for example.

Strengthening cybersecurity

Detecting anomalies will always be an important factor across security operations, especially when it comes to cybersecurity risks. System health dashboards can help identify camera tampering, and extra protection mechanisms built into infrastructure appliances can ensure that systems and networks stay hardened. Machine learning can be used to identify and block known and unknown malware from running on endpoint devices, strengthening antivirus protection on appliances.

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Detecting vehicle license plates

Modern automatic license plate recognition (ALPR) systems do more than just read license plates. They help streamline parking, track wanted vehicles, and efficiently monitor traffic flow. The Genetec Cloudrunner™ vehicle-centric investigation system does this and more. How? By pairing smart cameras with the power of the cloud.

The Cloudrunner CR-H2 camera is a solar-powered device that collects detailed vehicle data. It identifies vehicle attributes, such as color and type, and can also analyze behaviors, including speed and direction of travel. This cloud-powered approach helps investigators narrow their searches quickly, while enabling access to data from anywhere.

Why compliance with AI regulations matters

The potential applications of AI in security are exciting. But as this technology evolves, so do the risks. Unfair societal bias, developer bias, or model bias can impact critical decisions. Personal information can be used in ways that disregard data protection and privacy. In fact, a recent IBM report found that only 24% of generative AI solutions are secured.

Security system users are becoming aware of these risks. Our 2026 State of Physical Security Report found that 70% of end users worry about the design and implementation of AI systems, specifically how they might compromise data privacy. Only 29% of end users had no concerns about AI at all.

As more AI security risks surface, governments are drafting legislation to regulate how organizations can develop and implement AI-enabled technology. The goal is to protect individual rights without hampering technological advancements and trust.

For example, the 2024 AI Act in the European Union (EU) sets obligations for various AI applications based on their identified risk category. These requirements include creating adequate risk assessments and mitigation practices, using high-quality training datasets to reduce bias, and sharing detailed documentation on models with governing authorities as needed. In the most extreme cases, failure to comply with this new legislation can cost companies up to 7% of their global annual turnover.

The General Data Protection Regulation (GDPR) is also honing in on the security of AI applications. This legislation requires explicit consent from data owners to use their personal information for the development of AI models. AI systems must also be designed with privacy in m ind, while ensuring AI-related decisions are easily explainable to impacted users.

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It’s essential to comply with these mandates when developing AI technologies. Capitalizing on intelligent solutions should not come at the expense of responsible usage, ethical standards, or privacy compliance.

Best practices to ensure responsible AI use and compliance

• Conduct risk assessments: Evaluate how automating a specific process may impact critical systems or safety protocols

• Identify non-critical applications: Start by implementing AI into processes that aren’t central to your most critical operations to curb major business disruptions

• Prioritize human-centered design: Ensure that AI applications always empower humans with the information they need to make the best decisions

• Take advantage of privacy analytics: Deploy built-in privacy features within AI systems to limit and protect access to sensitive information

• Broaden data protection strategies: Apply cybersecurity measures and best practices to AI-enabled solutions, including regular audits and system updates

• Choose trusted vendors: Work with vendors who follow responsible AI principles, considering biases, data protection, and cybersecurity