As artificial intelligence continues to dominate vendor discussions, edge computing has emerged as a frequently misunderstood concept, particularly regarding its relationship to cloud computing and the long-term implications of either architecture for specific use cases.

Let’s examine edge and cloud processing in detail, including the benefits and trade-offs of each, to guide your continued efforts to build an optimal, future-ready security stack. 

What Is Edge Processing, and How Is It Different From Cloud Processing?

Both processing architectures have been around since the 1990s, although their conceptual roots date back to the 1960s. They are different approaches to sharing and processing data across devices.  

Cloud processing is the act of transferring data from a device to the cloud, where it is processed by advanced AI models.

Edge processing, also called edge computing, describes executing AI as close to a device as possible.   

Cloud processing became mainstream with Amazon Web Services and the democratization of cloud infrastructure, which catalyzed SaaS. Although edge computing has been around longer, it has been overshadowed by the cloud over the past few decades, and rightfully so. 

The need for data aggregation, lower IT overhead, and efficient storage and compute costs made cloud processing a no-brainer for most organizations. However, the proliferation of physical AI and continued shift toward sensor fusion of IoT-connected devices and cameras have gradually drawn focus back to the inherent benefits of edge processing. 

Cloud and Edge Processing Affect Security Orchestration

Cloud and edge processing present considerable trade-offs for security operations at scale, especially given the increase in AI workloads. 

Every AI request requires tokens, which incur highly variable fees from the companies providing these services. Where a security device processes its data and performs AI carries financial and security costs that demand careful planning to minimize overall risk and preserve a team’s budget.

Cloud AI is often faster and more powerful than edge AI because it is performed in data centers with large compute capacity and the most advanced models. Edge processing is typically used on smaller-scale hardware given its limitations; however, it presents distinct advantages in cost and response time for specific device use cases:

• Advanced AI image processing involves token fees that can quickly escalate, making edge processing a long-term cost advantage.
• Edge processing can have lower latency because data does not have to be sent to and from the cloud.
• Communication costs rise when devices must connect to a cloud hub or security operations center for all data processing.

Each of these factors can be detrimental to overall security effectiveness. In true preventive security, IoT devices must be able to detect suspicious activity, assess the threat level, and reason through potential actions. A holistic intelligent site management ecosystem can span speakers, lights, access control systems, and other devices. 

Edge processing ensures optimal response time in pressing situations, such as a camera identifying an intruder and triggering an alarm system and door-locking mechanism. If these devices use cloud processing, they would be rendered useless if network communications are disrupted or delayed. 

Milliseconds matter for executing a security response and giving a timely perception of control. If executed correctly, edge processing has an inherent edge in speed and reliability. 

How to Decide the Right Compute Architecture for Security Investments

Traditionally, security technologies consisted entirely of on-prem compute terminals built for direct human interaction; however, on-prem computing is not built for enterprise scale that requires data aggregation and agentic insights to be brought together securely across all locations.  

Edge computing was not made for human interaction, instead designed for compute devices like cameras, IoT sensors, and actuators. Edge computing orchestrates inference, sensor fusion, reasons, and responses. 

Cloud computing can deliver applications like on-prem terminals with greater agility and lower cost-of-ownership, and it removes the need for dedicated resources to install, manage, and upgrade on-prem technology across the enterprise. The cloud naturally aggregates data and, with advanced AI, can enrich, reason, and perform work in dynamic and non-deterministic ways.  

For physical security, as powerful as the cloud is, it can also be overused (or incorrectly used). For example:

• If a camera is used solely for video streaming and evidence recording, then cloud only processing is sufficient.
• If a camera or device is used to identify specific conditions and then trigger a standard set of actions, edge processing will ensure the most reliability and the shortest possible response time.
• If a camera or device is used to identify specific conditions and assess the appropriate response for each, sometimes needing to refer to more sophisticated processing and analysis, an edge-cloud hybrid processing will be advantageous for complex reasoning, at the trade-off of potential response time. 

The optimal setup is an edge-cloud hybrid solution where users can use a web or mobile application, and the edge device is solely a data processing and orchestration device. This architecture is future-proof because it is tightly integrated and AI capable while enabling seamless yet secure user access. This will also lower the total cost of ownership because AI can be handled on the device, minimize data transfer fees, and doesn’t need costly integrator or IT staff to install, support, and upgrade. 

Effective Security Enables Deterrence and Timely Response

As you optimize your spend, ensure that each device or system builds a holistic view of your site and overall enterprise, and can connect for orchestrated response. A security strategy focused on overall deterrence and timely response will remain resilient no matter what threats you face.

Devices that overwhelm security teams with alerts or fail to deliver urgent results when they matter are easy places to first assess.

Edge processing will help teams bolster security directly on site and minimize overall spend, however, cloud processing will remain integral for management, aggregation, and accessing the most advanced AI capabilities. The best ecosystems will leverage both, and we will soon see powerful use cases and success stories that help us all reap the full benefits of our industry’s evolution.