The Opaque Confidential AI Platform instantly protects ML pipelines with no re-engineering
Opaque Systems today released the first and only Confidential AI Platform for securely accelerating general-purpose AI workloads into production. Unveiled at the second annual Confidential Computing Summit, Opaque’s platform now supports machine learning (ML) pipelines as well as popular languages and frameworks for AI, such as Python and Spark. With Opaque, enterprises can run a wide range of AI workloads, including SQL analytics and ML inference, on encrypted data with no re-engineering required.
Generative AI holds immense business potential, but security and privacy concerns abound. As a result, the most valuable enterprise data is trapped in red tape, and existing techniques to operationalize that data securely—like anonymization—have proven error-prone and inadequate. Enterprises are forced to choose between innovation and security, preventing AI projects from coming to fruition. Opaque is the first and only company to help overcome these challenges and unlock the full value of organizations’ data.
“Enterprises have long been forced to choose between data security and extracting value from their data. With Opaque, that dichotomy ends,” said Aaron Fulkerson, CEO of Opaque. “Our platform ensures complete data sovereignty while allowing seamless deployment of AI workloads, unlocking business insights securely and efficiently–often enabling the use of sensitive data that couldn’t be used before.”
“Opaque offers a breakthrough for organizations struggling with the tension between innovation and security. By embedding privacy and security into every step of the ML pipeline, we enable enterprises to accelerate AI adoption confidently,” said Chester Leung, co-founder and Head of Platform Architecture at Opaque. “Our confidential AI platform uniquely enables the processing of encrypted data without a noticeable performance hit at cloud scale. With Opaque securing entire data workloads, companies can unlock new business opportunities and manage risks effectively, all while maintaining absolute control and privacy of their data.”
