#secure data processing

Data virtualization is the core technology that allows for fully compliant and secure data processing across expansive, spread out networks. A virtual representation of data is combined and processed securely according to the existing business rules. This creates an interoperable layer where analytics can be conducted on custom datasets, regardless of where the data is stored, eliminating the need for slow and costly data transfers.

These issues go beyond the violation of intellectual property rights and have strong financial repercussions. When NFT copyright content is stolen or pirated, the uniqueness and value associated with the assets are greatly degraded. This not only undermines the trust and confidence of NFT creators and collectors but also can result in substantial financial loss.

Organizations that wish to maximize the value from their data, comply with regulations, and minimize risk need to ensure secure data processing. Fortunately, Intertrust offers data management technology that not only improves security but also increases the speed of insight.

Organizations that wish to maximize the value from their data, comply with regulations, and minimize risk need to ensure secure data processing. Fortunately, Intertrust offers data management technology that not only improves security but also increases the speed of insight.

A data virtualization platform can be added to your existing data architecture without altering your data storage functions, whether they are in the cloud, on-site, or a combination of both. A virtualized data layer offers secure data processing with comprehensive data governance and access control, letting administrators set permissions down to the row and column level.

Data virtualization is the core technology that allows for fully compliant and secure data processing across expansive, spread out networks. A virtual representation of data is combined and processed securely according to the existing business rules. This creates an interoperable layer where analytics can be conducted on custom datasets, regardless of where the data is stored, eliminating the need for slow and costly data transfers.