Data Infrastructure

Opaque Unveils Platform for Running AI Workloads on Encrypted Data

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.”

Now integrated with Confidential VMs as well as Kubernetes, Opaque supports popular AI workloads for high-performance, cloud-scale deployments. These new capabilities expand on Opaque’s unique ability to facilitate secure collaboration with cryptographic verification of privacy. Organizations across industries are empowered to unlock sensitive data, yield richer insights, and drive tangible business value because AI workloads can be deployed on data while encrypted. Use cases include:

  • High Tech: Unblock AI projects by securing data pipelines for analytics and ML workloads, ensuring sensitive and customer data is kept private, and dynamically training models with customer data while encrypted.
  • Financial Services: Enable secure data sharing and collaboration across business units and partners, ensuring privacy and regulatory compliance. Remove the need for insecure and time-consuming data prep and masking, delivering fast time to value and better-quality insights, ultimately enhancing data-driven decision-making.
  • Manufacturing: Manufacturers deploying Generative AI use Opaque as a confidential control plane to enforce data governance rules. This provides verifiable evidence that data policies, privacy, and sovereignty were maintained.
  • Human Resources: HR departments across industries use Opaque to securely share and analyze employee data across multiple data silos. This enables workforce analyses to improve recruitment, retention, compensation, and talent development while ensuring compliance with data privacy regulations like GDPR. Data-driven decisions are made without compromising employee confidentiality.

“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.”

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