Teaching AI New Tricks

Redefining the role of AI-enabled security cameras

  • By Adam Lowenstein
  • Mar 04, 2025

You have probably heard that AI-enabled security cameras are evolving the role of traditional surveillance cameras, shifting the focus from passive monitoring to active problem-solving and operational insights. AI technology changes fast, so what is new can be considered only news in just a few months.

In the security field, the latest cameras have evolved into smart IoT sensors capable of processing a wealth of data at the edge (in-camera). Through advancements like on-site learning and open-platform architectures, these devices are taking on new responsibilities, from recognizing unique, custom objects to hosting diverse third-party applications that address specialized challenges.

These “new tricks” mark a significant milestone in the field of professional security, emphasizing flexibility, intelligence and collaboration.

On-site Learning: Empowering Customization
One of the most groundbreaking features of AI-enabled cameras is on-site learning, which allows users to train cameras to recognize objects or scenarios specific to their environments. Unlike conventional pre-programmed analytics, this capability enables a hospital to track misplaced wheelchairs or count ambulances arriving at the emergency bay.

Similarly, a logistics company might teach its cameras to identify specific truck logos or monitor pallet movements at a loading dock.

This adaptability has implications far beyond security. By using on-site learning, data-hungry businesses can generate valuable operational metrics and key performance indicators (KPIs).

For instance, monitoring forklift activity in a warehouse can provide insights into workflow efficiency, while counting vehicles can support predictive maintenance or resource allocation. By embedding this intelligence at the edge — directly within the camera — organizations can reduce the need for centralized processing, ensuring real-time responsiveness and cost efficiency.

Hosting Specialized Applications with Open Platforms
Another innovation transforming use cases for security cameras is its ability to host third-party applications through an open-platform architecture. This approach gives developers and integrators the freedom to create bespoke solutions that address unique challenges.

For example, a camera could host an application designed to detect individuals who have fallen — identifying a horizontal human form in a space where people are expected to remain upright. Similarly, applications can be developed for detecting smoke or leaks in outdoor environments where traditional sensors might fail.

Key to this flexibility is the use of standardized container technologies, such as Docker, which enable modular, secure and scalable application development. Containers encapsulate applications within a protective layer, preventing unauthorized access to the camera’s core functions while facilitating seamless integration with cloud services. Developers can design and deploy their applications in cloud environments and then run them on any compatible device, whether on the edge or in the cloud.

By using containerized architectures, security professionals gain the flexibility to customize their systems for specific use cases while maintaining cybersecurity standards. Modular containers also simplify updates, enabling users to implement new features or improvements without disrupting existing operations. This capability aligns with broader digital transformation efforts, making AI-enabled cameras more than just security tools — they become essential IoT devices supporting a range of business functions.

Extending the Life of Legacy Systems
The ability to integrate AI capabilities into existing infrastructure is another key development in the evolution of AI-enabled cameras. By processing AI analytics at the edge, newer systems can enhance the functionality of legacy devices, such as older network cameras. This approach reduces the need for full-scale replacements, which can be both costly and disruptive.

For instance, edge devices can add AI metadata to non-AI cameras, enabling features like object recognition or event detection without requiring physical upgrades. This is particularly beneficial in scenarios where cameras are in hard-to-reach areas, such as industrial facilities or large campuses.

Extending the lifespan of existing hardware not only maximizes return on investment but also minimizes waste, and reduces downtime, all while aligning with sustainability goals.

The Open-platform Advantage
In an industry often divided between closed and open systems, open-platform architectures are emerging as a clear winner for organizations seeking adaptability and long-term value. Closed systems, while straightforward to deploy, often lock users into proprietary ecosystems, limiting their ability to adopt new technologies or integrate third-party solutions.

Open platforms, on the other hand, encourage collaboration and innovation by providing developers with access to tools like software development kits (SDKs), application programming interfaces (APIs), and open development platforms like Docker. These tools allow users to integrate a wide range of applications, ensuring their systems remain future-proof and capable of adapting to evolving needs.

For example, cameras built on open platforms can host applications from various developers, much like a smartphone runs multiple apps. By embracing open-platform technologies, organizations avoid the constraints of one-size-fits-all solutions and gain the ability to build tailor-made systems that meet their unique requirements.

The Future of AI-enabled Security Cameras
As AI-enabled security cameras continue to evolve, their role is expanding beyond traditional surveillance. On-site learning and open platforms are transforming these devices into versatile tools that contribute to both security and operational efficiency.

By allowing cameras to learn from their environments and host specialized applications, organizations can address challenges that were previously beyond the scope of traditional security systems.

This shift highlights the growing importance of edge intelligence and modular design in the security industry. By leveraging these advancements, businesses can not only improve their security posture but also gain valuable insights that support broader organizational goals. Whether it is tracking assets, enhancing safety protocols, or generating actionable data, the potential applications for AI-enabled cameras are virtually limitless.

For security professionals, the message is clear: the future of AI in security lies in adaptability and collaboration. By embracing open platforms and evolving capabilities at the speed of innovation, we can teach AI new tricks that uniquely benefit every organization.

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