Edge Data

Preserving Edge Data Security: Cloud-to-Edge Data Automation

Even though a lot of today’s data is moving to the cloud, there are many cases when data needs to stay at the edge for ...

Even though a lot of today’s data is moving to the cloud, there are many cases when data needs to stay at the edge for security and privacy reasons.

A challenge for technology vendors and technology adopters

When data needs to stay at the edge, on-premise solutions become a necessity, which is a challenge for both technology vendors and technology adopters. For technology vendors, creating an on-premise solution means they will have to make compromises on the capabilities of their product, their ability to perform updates, and their ability to support their solutions. For technology adopters, using an on-premise solution means their solutions will be siloed, and they will be difficult to manage and coordinate across multiple sites. Advanced capabilities from the cloud are also out of question since they are physically limited to the edge. These limitations will slow down the adoption of technology and the impact of digital transformation efforts. While cloud-based solutions are charging full-speed ahead, on-premise solutions are lagging behind. 

Modern data automation solutions must address edge solution challenges

Modern data automation solutions need to address the concerns of both technology vendors and technology adopters in order to bring edge solutions into the forefront of digital transformation. 

Solutions that allow users to keep their data entirely at the edge while still giving them the freedom and flexibility to take advantage of the vast capabilities of the cloud are key to keeping data transformation solutions futureproof.

Enterprise users who work with massive amounts of edge data can build data workflows in the cloud and deploy them to run at the edge with cloud-to-edge data automation platforms like Prescient Designer. The benefit of this approach is that it gives users the choice to keep all their data at the edge while still giving them access to the vast capabilities of cloud computing at the same time. 

Best of both worlds: collaboration in the cloud, and data security at the edge

By managing workflows in the cloud, users will have access to these workflows anywhere across the enterprise, maximizing the opportunity to optimize workflow solutions and team collaboration. Data models, AI models, and data transformation workflows can be shared and improved upon by all departments. At the same time, user access can be logged and tracked to create consistency, compliance, and collaboration.

Not only does this improve collaboration within the organization, but it also enables further collaboration between technology vendors and technology adopters. Technology vendors can build their solutions in the cloud without needing access to customers' data in the cloud. They can easily discuss solutions with customers by logging into the same data automation software from different locations. This also allows them to build dashboards to monitor activities taking place at the edge, while sharing access to end customers to keep them informed. End customers will also have access to the most advanced technology solutions without having to worry about data security and data privacy concerns.

Edge data will never leave the local site

Once workflows are created in the cloud, they can be deployed to run at the edge - on edge gateways, edge PCs, or edge GPUs, depending on the workload required. The edge data is processed in these workflows, and the edge data itself does not need to leave the local site. Thus, the edge data solution can stay entirely on-premise while non-sensitive information, such as system status and alerts, can be sent to the cloud dashboards for better visualization.

How are cloud-to-edge data solutions used in application?

An example use case is one where a technology vendor creates an AI solution to perform predictive analytics.  The technology vendor builds both the data workflow and the AI model in the cloud.  Because the solution is in the cloud, the technology vendor can update the workflow and the AI model any time, and then push these updates to the end customer.  The data workflow runs locally at the end customer’s site, eliminating the problem of security and privacy. The technology vendor can still collect usage information, at the end customer’s consent, to continuously improve its product.

This solution architecture created a path for future innovation. End customers can coordinate data analytics from multiple sites, removing silos and improving organizational data management. Federated learning can also be used, so AI models can be trained at the edge using local datasets and later combined to form a common AI model in the cloud. Then, they can be deployed back to run at the local sites as an optimized AI model.

Organizations that adopt a cloud-to-edge data automation solution can also ensure that certificate-based authentication and encryption are used on these connections. Workflow deployments using an outbound client port, such as 443 for secure HTTPS or 8883 for secure MQTTS, allows a local appliance to initiate a connection to the outside, but does not allow any outside appliances to connect in. Organizations can also restrict the destination address so the outbound connection can only go to its designated destination. Hence, enterprises can make their edge solutions both safe and accessible.

The future of cloud-to-edge data automation is here

Cloud-to-edge data automation bridges the gap between edge data security and the benefits of cloud infrastructure. It allows technology vendors and technology adopters to share and collaborate edge data solutions for continuous improvement and long-term optimization across the enterprise.

To learn more about how your team can also implement an cloud-to-edge data automation solution for your end customers, get in touch with us directly, and we'll help you answer your unique edge data questions


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