Cube AI is an open-source framework designed to enable the secure deployment of Large Language Models (LLMs) in privacy-sensitive applications by leveraging Confidential Computing. It utilizes Trusted Execution Environments (TEEs) to safeguard user data and AI models, ensuring confidentiality and integrity during processing. By isolating sensitive computations within secure enclaves, Cube AI protects against unauthorized access and tampering while supporting both open-source LLMs, such as Llama and Falcon, and proprietary models. This makes Cube AI a versatile and reliable solution for industries like healthcare, finance, and enterprise AI, where data security and compliance are paramount.
Cube AI uses Trusted Execution Environments (TEEs) to protect both user data and AI models from unauthorized access, ensuring data confidentiality and code integrity during execution.
Cube AI is agnostic to the underlying model, supporting a range of popular open-source LLMs like Llama, Falcon, and Mistral, as well as proprietary models, offering great flexibility for diverse applications.
Cube AI provides fine-grained access control, allowing you to manage user permissions with role-based or attribute-based access. This ensures that only authorized users can access sensitive workloads and data, enhancing security.
Cube AI is designed to handle large-scale workloads and demanding AI applications, providing high-performance capabilities while ensuring privacy and security for users.
Cube AI ensures that all traffic is encrypted end-to-end, safeguarding sensitive data during transmission between systems, providing secure communication for your AI-powered applications.
Cube AI provides a user-friendly SDK and API, making it easy to integrate with existing systems and AI workflows, enabling secure deployment of AI models without extensive rework.
Cube AI includes a robust remote attestation mechanism that ensures the integrity of the system during execution, verifying that AI models are running in a trusted environment, even in distributed or untrusted networks.
Cube AI is open-source and released under the Apache 2.0 license. This promotes transparency, collaboration, and innovation in the developer community, empowering users to customize and contribute to the platform.
Explore Cube AI:
Visit Cube AI GitHubCube AI delivers a groundbreaking architecture focused on protecting large language models (LLMs), user prompts, and associated data through Trusted Execution Environments (TEEs). It ensures the secure handling of sensitive requests and datasets, empowering organizations to confidently deploy and use advanced AI models in a protected environment.
1. User Management and Access Control: Cube AI includes robust user management features, allowing granular access control for sensitive LLM operations. Role-based permissions ensure that only authorized users can interact with protected models and data.
2. Flexible Model Deployment: The platform supports dynamic deployment of AI models, enabling users to upload, manage, and execute models securely. This flexibility allows organizations to adapt to changing needs while ensuring the confidentiality of their intellectual property.
3. HAL (Hardware Abstraction Layer): The Hardware Abstraction Layer provides seamless interaction with a variety of TEE-enabled hardware platforms, including AMD SEV and Intel TDX. This abstraction ensures consistency and efficiency in managing secure computations across different infrastructures.
4. Private and Public Cloud Support: Cube AI is designed for hybrid environments, allowing deployment in both private and public clouds. This flexibility ensures scalability and meets diverse operational requirements while maintaining data privacy and security.
Cube AI empowers organizations to deploy any Large Language Model (LLM) securely and efficiently, integrating seamlessly with leading platforms like Ollama and Hugging Face.
With Cube AI, you can protect sensitive user prompts and data by leveraging Trusted Execution Environments (TEEs). This ensures that your AI applications not only perform optimally but also uphold the highest standards of confidentiality and security.
Whether you're working with pre-trained models or fine-tuning your own, Cube AI simplifies the deployment process, enabling secure, scalable, and compliant AI solutions for diverse applications.
The AI Gateway is a critical component of Cube AI, delivering built-in Security, Observability, and Governance to AI workloads. It is powered by CubeProxy’s Policy Enforcement Point (PEP) service, optimized for proxying API calls to Large Language Models (LLMs).
This gateway safeguards API communications by implementing advanced security measures, ensuring that data flows and requests to LLMs are handled with confidentiality and integrity. It also provides robust observability, enabling real-time monitoring and logging of API interactions for compliance and operational insights.
In addition, the AI Gateway enforces governance policies, allowing organizations to control access, usage, and compliance with internal and external regulations. By integrating seamlessly with Cube AI, it enhances the trustworthiness of LLM applications while maintaining high performance and scalability.
Process sensitive patient records securely and generate valuable insights for diagnostics and research without exposing private data. Cube AI ensures compliance with stringent healthcare privacy regulations.
Analyze confidential financial transactions, detect fraud, and produce secure financial reports with Cube AI's robust protections for sensitive data and computational integrity.
Deploy intelligent chatbots capable of handling sensitive user queries, providing personalized and private support, backed by Cube AI's secure data processing capabilities.
Build proprietary AI tools and workflows with confidence. Cube AI ensures the security of both proprietary models and sensitive enterprise data during AI development and deployment.
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