Opaque Systems, an AI and analytics company, has unveiled new advancements for its confidential computing platform, prioritizing the confidentiality of organizational data in conjunction with large language models (LLMs).
The company’s latest offerings will be showcased during Opaque’s keynote address at the inaugural Confidential Computing Summit, scheduled for June 29 in San Francisco.
Among the innovations are a privacy-preserving generative AI optimized for Microsoft Azure’s Confidential Computing Cloud and a zero-trust analytics platform called Data Clean Room (DCR). Opaque Systems integrates secure hardware enclaves and unique cryptographic fortifications to provide multiple layers of protection for its generative AI, ensuring data remains encrypted throughout model training, fine-tuning, and inference stages.
Jay Harel, VP of product at Opaque Systems, emphasized the platform’s commitment to data security, stating, “Our platform safeguards data at rest, in transit, and while in use, minimizing the likelihood of data breaches throughout the lifecycle.”
These advancements aim to enable organizations to securely analyze confidential data while ensuring its confidentiality and safeguarding against unauthorized access. Opaque achieves this by executing machine learning and AI models on encrypted data within trusted execution environments (TEEs), preventing unauthorized access to sensitive information.
Furthermore, Opaque’s Data Clean Rooms (DCRs) operate on the principle of zero-trust, encrypting data at rest, in transit, and during usage. This comprehensive approach ensures that data remains confidential throughout the entire process.
To fully unleash the potential of LLMs like ChatGPT, Opaque highlights the need to train these models on confidential data without the risk of exposure. The company recommends adopting confidential computing, which safeguards data throughout the model training and inference process, unlocking the transformative capabilities of LLMs.
Opaque utilizes Confidential Computing technology to leverage specialized hardware provided by cloud providers. By encrypting datasets end-to-end throughout the machine learning lifecycle, Opaque’s platform ensures the privacy of the model, prompt, and context during training and inference.
Harel identified three main issues concerning generative AI and privacy, particularly with LLMs:
- Queries: LLM providers have visibility into user queries, potentially accessing sensitive information such as proprietary code or personally identifiable information (PII). This concern is amplified by the increasing risk of hacking.
- Training models: Providers often access and analyze internal training data to enhance AI models. However, retaining training data can accumulate confidential information, heightening vulnerability to data breaches.
- IP issues for organizations with proprietary models: Fine-tuning models using proprietary data requires granting LLM providers access to sensitive information or deploying proprietary models within the organization. This exposes private and sensitive data to external individuals, raising the risk of hacking and data breaches.
Opaque Systems has developed its generative AI technology with these concerns in mind, aiming to facilitate secure collaboration among organizations and data owners while ensuring regulatory compliance. With Opaque’s platform, organizations can train, fine-tune, and run inference on LLMs without gaining direct access to the raw data itself, preserving data privacy.
The company’s Data Clean Room (DCR) offering is reinforced with secure hardware enclaves and cryptographic fortification, delivering multiple layers of protection against cyberattacks and data breaches. Operating within a cloud-native environment, the system executes within a secure enclave on the user’s cloud instance, allowing businesses to retain their existing data infrastructure.
Harel emphasized Opaque’s mission to prioritize the privacy of confidential data, stating, “For AI workloads, we enable businesses to keep their data encrypted and secure throughout the lifecycle, significantly reducing the likelihood of loss. Data is kept confidential at rest, in transit, and while in use.”
Opaque Systems innovative advancements in confidential computing provide organizations with robust solutions for data privacy and secure analytics, empowering them to leverage AI technologies while maintaining the confidentiality of their data.
