The Analytics and AI Challenge in Confidential Computing

Enclave innovation protects data from attack and unauthorized access, but it also presents immense challenges and obstacles to performing analytics and machine learning within and across teams. The inability to securely share data or analyze data that is owned by multiple parties has resulted in organizations having to restrict data access, eliminate data sets or mask specific data fields and outright prevent any level of data sharing. The implication for organizations is the inability to execute on numerous use cases across verticals while the urgency increases to get answers from the data. Example use cases that have been challenging include: collaborating to identify and prevent money laundering in financial services, confidentially sharing patient information for clinical trials, and sharing sensor data and manufacturing information to perform preventive maintenance applications.

These significant challenges have inhibited organizations from achieving faster value from confidential computing technologies and have inhibited getting answers from data that is locked up in data silos. There is an urgent need to overcome the challenges and unlock the data to deliver on critical business needs. These include:

  1. Protecting data in use – encryption end-to-end
  2. Securely setting up a cluster of enclaves, including secure key distribution, integrity of enclaves, and secure inter-enclave communication
  3. Protecting data end-to-end; from data sources into enclaves and analytics
  4. The need for specialized skills and in-house development to create analytic apps & ML frameworks that leverage secure enclaves
  5. Inability to share data and run secure collaborative analytics across multiple parties (inter- and intra-company)
  6. Meeting regulatory compliance policies while sharing data across entities (e.g., PII data)

The Challenge

Securing data and preventing cyberattacks pose many challenges for organizations today. Encrypting data at rest and in motion is effective but incomplete. Data is open to attack during processing and needs to be protected. Organizations also need to verify the integrity of the code to prevent unauthorized access or exploits. While data needs to be protected, it also needs to be effectively and appropriately shared and analyzed within and across organizations.

What’s Needed

Addressing these challenges requires a comprehensive, integrated platform that enables analytics at scale on encrypted data and secure collaborative data sharing within and across organizations. A solution that uniquely secures data at rest, in motion, and during processing at scale. A solution that also supports confidential access and enables advanced analytics and ML within and across company boundaries.

The technology our platform is based on was created at UC Berkeley by world-renowned computer scientists. The original innovations were released as open-source and deployed by global corporations in banking, healthcare, and other industries. Opaque Systems was founded by the creators of the MC2 open source project to turn it into an enterprise-ready platform, enabling analytics and AI/ML on encrypted data without exposing it unencrypted. One of the major benefits of the Opaque platform is the unique capabilities around collaboration and data sharing, allowing multiple teams of data owners to collaborate, whether inside a large organization or across companies and 3rd parties. Opaque is the first scalable confidential computing platform for collaborative analytics, AI, and data sharing that lets any user or entity collaboratively analyze confidential data while still keeping the data and the analytical outcomes private to each party.

High-Performance Analytics and AI/ML on Encrypted Data

The Opaque Platform allows you to run analytics and ML at scale on encrypted data while collaborating securely within and across organizational boundaries. Using our platform, you can upload encrypted data or connect to disparate encrypted sources. You can then edit and execute high-performance SQL queries, analytics jobs, and AI/ML models using familiar notebooks and analytical tools. Verifying cluster deployments via remote attestation becomes a single-click process.

The platform makes it easy to establish confidential collaboration workspaces across multiple users and teams and combine encrypted data sets without exposing data across team boundaries. It takes away the hassle of setting up and scaling enclave clusters and automates orchestration and cluster management. On top of that, the Opaque Platform leverages multiple layers of security to provide defense in depth and fortify enclave components with cryptographic techniques, using only NIST-approved encryption.

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