Article: Smart AI in Action - The Edge vs. Cloud Debate in Video Security

By Katherine Balabanova, 3dEYE, and Sascha Kylau, OneTel Security

The Key Pros and Cons of Cloud AI and Edge AI

Security Considerations

One of the most significant advantages of Cloud AI over Edge AI is security. Cloud platforms used in large-scale video monitoring—such as AWS, Google, or Azure—leverage robust infrastructure with advanced security protocols. These include encryption, multi-factor authentication, and regular updates to guard against emerging threats. Centralizing data in the cloud ensures that real-time video streams, archives, and event logs are consistently protected under the highest security standards.

Advantages of Cloud AI:

• Minimal edge attack surface

• No physical servers to protect

• No black-box devices in the on-premises network that could be compromised

• Direct-to-cloud encrypted connections eliminate the need for port forwarding, firewalls, or exposed devices

• Project security responsibility shifts from local IT personnel to the cloud provider

• Centralized software updates and security patches

• Centralized monitoring and AI anomaly detection

• Centralized Identity Access Management (IAM)

• End-to-end data encryption (e.g., AWS standards)

• Flexible KMS encryption for custom data protection keys

• High data durability (e.g., AWS S3 provides 11 9s durability)

• Comprehensive compliance support

Disadvantages of Cloud AI:

• Recurring costs associated with storing data on the cloud

• Requires sufficient network bandwidth for uploading and downloading large datasets

Edge AI Challenges and Limitations

Edge devices, while capable of localized processing, face constraints due to limited computational resources. These resources must be divided among tasks like archiving, decoding, streaming, and AI processing, often resulting in compromises:

• Restricted AI model size and complexity

• Reduced number of AI modules that can run concurrently

• Limitations on the amount of video processed simultaneously

Additionally, Edge AI devices often require frequent upgrades to support new and more computationally intensive AI models. They also pose a significant IT burden:

• Edge devices have a similar attack surface to on-premises servers but lack IT transparency, making them “black boxes.”

• If compromised, IT personnel may have no way to control or even detect the issue.

• Multilocation installations increase the strain on IT teams, requiring separate networks, firewalls, and monitoring systems for each edge device.

“Cloud AI’s ability to process vast amounts of data in real-time is a game-changer for video monitoring.”

Scalability and Flexibility

Cloud AI platforms are designed for scalability, making them ideal for large-scale video monitoring. Unlike edge devices, which are hardware-constrained, cloud systems can expand their processing power and storage capacity as demand grows.

As AI capabilities advance, especially in computer vision and large language models (LLMs), Cloud AI effortlessly handles increasingly complex scenarios. This scalability is crucial for monitoring applications that must process ever-growing data volumes without compromising performance.

Key Growth Factors Driving Cloud AI Adoption:

• Security camera installations are projected to grow from 1.2 billion to 2 billion by 2030.

• The $100 billion alarm monitoring market is shifting toward cloud-connected cameras.

• The Video Surveillance as a Service (VSaaS) market is growing rapidly, with 30% year-over-year growth in cloud-connected cameras.

As AI continues to grow exponentially, Cloud AI’s scalability, flexibility, and enhanced security make it the optimal choice for video security solutions.

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