Anjuna Secure Cloud Computing and Confidential AI Platform

What is Anjuna?

Anjuna offers a universal confidential computing platform designed to secure applications and data within public cloud environments. It facilitates the execution of any application across major clouds like AWS, Azure, and Google Cloud, ensuring complete data security and privacy without requiring code modifications. The platform utilizes Trusted Execution Environments (TEEs) or secure enclaves to isolate data, code, and secrets, protecting them from unauthorized access, even from privileged users or the cloud provider itself.

Leveraging this secure foundation, Anjuna enables Confidential AI and data collaboration through products like Anjuna Northstar, an AI Data Fusion Clean Room. This allows multiple parties to securely merge and analyze sensitive datasets using AI models while maintaining the confidentiality of each party's intellectual property and data. It addresses challenges in industries requiring high data security, such as financial services, healthcare, and blockchain, enabling secure cloud migration, risk elimination, and innovative product development.

Features

Isolation with TEEs: Creates Trusted Execution Environments (secure enclaves) to isolate data, code, and secrets.
Data Encryption: Encrypts data in use, at rest, and in transit within the confidential computing environment.
Cryptographic Attestation: Verifies the integrity of infrastructure and applications before granting access to secrets.
Cross-Cloud Compatibility: Runs applications across AWS, Azure, and Google Cloud without code changes.
AI Data Fusion Clean Room (Northstar): Provides a private environment for secure, AI-driven multi-party data analysis and collaboration.
Universal Platform (Seaglass): Enables running any application in any cloud with complete data security and privacy.

Use Cases

Migrating sensitive workloads to the public cloud with enhanced privacy controls.
Building differentiated products with intrinsic security and privacy features.
Eliminating data exposure risk for applications and data processing in the cloud.
Enabling secure multi-party data collaboration and analysis without compromising data privacy.
Securing AI model training and inference on sensitive, combined datasets.
Ensuring compliance with data privacy regulations during cloud operations.