Investing in connected, well-managed infrastructure not only improves operations but also creates stronger data foundation for future AI initiatives.

When it comes to AI in the physical security industry, much of the conversation has rightfully focused on analytics. With devices now doing more than standard recording, many are configured to detect an object, recognize a particular behavior, or identify an event that requires immediate attention.

Those applications improve daily and will continue to remain important. However, the role of AI is expanding. Looking ahead, AI agents could assist with the administration and optimization of the physical security systems themselves, including identifying system issues, recommending configuration changes, supporting lifecycle management, or automating routine tasks.

Of course, it’s going to require more than increasingly capable AI models in order to unlock those possibilities. Organizations will also need trusted data, secure connectivity, strong governance, and an infrastructure that can support new applications as they emerge.

AI expands beyond analytics

AI has already changed what organizations can learn from their security data. For example, video analytics can help teams detect events and patterns that would be difficult to identify through manual monitoring and analysis.

The next phase will apply intelligence to a broader set of operational challenges. IT and security teams must support complex environments, often without changes in resources. AI can help by automating repetitive admin tasks and assisting teams with troubleshooting, system optimization, and other aspects of day-to-day management.

This represents a shift from using AI primarily to understand what is happening within a scene to exploring how AI can help organizations manage the technology itself. As these capabilities continue to develop, AI will become a tool for helping smaller teams oversee large and complex physical security environments.

Trusted data becomes the foundation

Of course, an AI system can only make useful recommendations or take appropriate actions if it can rely on the information that’s intended to support those decisions.

That makes data quality an essential part of preparing for more advanced AI. Physical security environments can include thousands of connected cameras, intercoms, access control devices, sensors, and other technologies. Each device generates data about events, configurations, performance, and system health. Accurate metadata and standardized information flows can make it easier for applications to interpret and utilize that data.

Connectivity and lifecycle awareness are equally important. Understanding whether a device is online, properly configured, and operating as expected provides context for making correct decisions.

Investing in connected, well-managed infrastructure does more than improve operations; it helps create a stronger data foundation for future AI initiatives.

“We may not know exactly how agentic AI will be applied to physical security five years from now, but we can start preparing today,” said Patrik Pettersson, Strategic Advisor, Incubation, Axis Communications. “The organizations that establish secure connectivity, trusted data, and well-managed devices will be in a much better position to take advantage of any new capabilities as they emerge.”

Why secure cloud foundations matter

As AI’s role expands from generating insights to potentially taking actions, trust and governance will become even more important.

Secure cloud platforms can provide an essential layer between devices, applications, and services. Platforms such as Axis Cloud Connect, for example, enable secure communication and connectivity across physical security environments, while also providing a foundation for services to interact with connected devices.

This foundation will become even more important as agentic AI evolves. Organizations will need visibility and control over who or what is allowed to access their systems, which services can interact with individual devices, and when those interactions can occur. An AI agent that recommends a configuration change presents one level of risk. Giving that agent permission to make the change itself introduces an entirely different risk and/or governance consideration.

Organizations will need confidence in the AI itself as well as the infrastructure controlling its access to critical systems. As intelligence becomes more capable, the mechanisms governing what it can see and do will become just as important.

Openness enables future innovation

There is another challenge: Nobody knows exactly what tomorrow’s AI ecosystem will look like.

Organizations may ultimately use different AI models, analytics providers, applications, and services for different purposes. An infrastructure built around open APIs and interoperability will provide greater flexibility to adopt those emerging capabilities.

This flexibility also reduces the risk of tying future innovation to active decisions. Rather than replacing entire systems to take advantage of a new AI capability, organizations can integrate new applications and services into an existing technology environment.

Open architectures also offer greater freedom to choose how and where organizations adopt AI while reducing the dependence on any single technology or provider.

Building for what comes next

The physical security industry’s AI conversation is moving beyond analytics and event detection. Increasingly, the opportunity will involve automation, orchestration, administration, and operational efficiency.

Exactly how those capabilities develop remains to be seen. But organizations need not know every future AI use case to begin preparation. By investing in secure connectivity, trusted data, strong governance, and open infrastructure, organizations can create an environment to support new emerging AI capabilities.

Learn more about Axis Communications.