Data in Use Protection: Why It’s Critical for Secure AI

Data in use protection is a critical cybersecurity measure that safeguards sensitive information during active processing, which traditional encryption methods for data at rest and in transit do not cover. As organizations increasingly rely on AI, cloud infrastructure, and cross-organizational collaboration, protecting data during computation becomes crucial, particularly in fields like healthcare, financial services, and government. Technologies such as Trusted Execution Environments (TEEs), Fully Homomorphic Encryption (FHE), and Secure Multi-Party Computation (MPC) play vital roles in securing data without exposing it during processing or sharing. These methods allow for secure AI training and analytics while maintaining compliance with privacy regulations and enabling collaborative efforts across various domains. Effective data in use protection often involves a combination of technologies and strong governance to ensure compliance, performance, and secure collaboration, as demonstrated by platforms like Duality, which integrate these capabilities to enhance secure AI and analytics workflows.

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