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Cape Privacy Launches Self-Service Enterprise Solution to Enable Secure Predictions

Cape Privacy, the platform that enables businesses to run predictive machine learning models on encrypted data, today announced the availability of its self-service enterprise solution optimized for the Snowflake data cloud. Businesses can now use Cape Privacy to employ encryption-in-use, securely operationalize their most highly classified data, and run predictive machine learning models on encrypted data stored in private clouds or in a third-party data cloud.

According to IDC, 68% of all data collected by organizations goes unused. One reason this happens is due to security concerns when accessing sensitive data. Traditionally, in order to gather value from encrypted data, it had to be decrypted, putting the plaintext data at risk of exposure to threat actors, or human or technical errors. This security gap prevents organizations from safely operationalizing data resulting in a missed opportunity to leverage that data in a way that benefits them and their customers.

“When dealing with the most sensitive information in the world, such as financial data, it is critical that security is prioritized. That said, the potential benefit to the customer when using that data for insights cannot be ignored,” said Ché Wijesinghe, CEO at Cape Privacy. “There is an enormous amount of highly sensitive personal, financial and health-related data and intellectual property generated and collected each day that is not adequately protected. This is a growing risk that we see being exploited all the time. Giving organizations the ability to maintain encryption at all times, they can close that risk gap by achieving encryption-in-use and finally put their most sensitive data to work safely. That is what Cape Privacy is doing.”

Encryption protects data, but limits the ability of artificial intelligence or machine learning technologies to access and operationalize the data. Cape Privacy solves this problem with a novel combination of secret sharing—a well-established cryptography scheme—and secure multi-party computation (MPC). This allows organizations to protect encryption keys and keep sensitive data encrypted even while running it through their chosen prediction model from their Snowflake environment.

“Cape Privacy solved a major problem by making the encrypted data useful without decrypting it. It’s a real gamechanger,” said Michael Aguiling, CTO at Cerberus Technology Solutions. “We were able to quickly get started and run analysis to understand our data and better deliver our product. We did all of this while maintaining encryption and therefore, privacy and security. It’s a win-win that has helped us be a better business.”

As a self-service, enterprise-grade platform, Cape Privacy empowers businesses to run as many data models as needed to gain the best possible insights using the best data available. The ability to run this type of modeling in a self service capacity is unique to Cape Privacy. Businesses benefit with:

  • Seamless Integration with Snowflake - Easy to access and deploy machine learning predictions directly within a Snowflake account.

  • Intuitive User Interface - Point and click operation provides an intuitive experience, no training required.

  • Model Flexibility -Supports the ONNX model standard, creating flexibility to choose whichever machine learning model is the best fit.

  • Client-Side Encryption - Data is client-side encrypted with AES before uploading to Snowflake. The data is never decrypted.

  • Cryptographic transparency - Delivers a visual graph of every query, showing each step of a model’s computation process. Documentation builds trust through transparency, and provides assurance of data security.

Cape Privacy will offer three usage tiers - free, standard and enterprise - to meet the needs of businesses of all sizes. For more information, Cape Privacy will be hosting a webinar, titled “Introducing Cape Privacy's Self Service Platform for Running AI Predictions on Encrypted Data in Snowflake” on March 2, 2022 at 11:30am PT/ 2:30pm ET. To sign up, visit:




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