AI Data Growth: Embedded Storage Security Risks Rise

The expansion of AI deployment is increasing storage security risks in embedded systems. Organizations are creating and retaining larger volumes of sensitive data across various environments, including industrial, medical, and edge computing. What's interesting is that as AI applications move beyond initial projects into operational systems, protecting stored data becomes a significant challenge for hardware designers. Apacer notes that the growing use of AI models and machine learning datasets creates new risks like data exposure and intellectual property theft. These concerns now extend to storage devices embedded at the network edge. The broader cyber threat landscape makes this issue more significant. The Department for Science, Innovation & Technology estimates cyberattacks cost the UK economy billions annually. To address these risks, Apacer developed CoreSecurity2. This technology aims to protect sensitive information on industrial SSDs and memory products, preventing unauthorized access throughout a device's lifecycle. It includes features like irreversible data destruction, complete data sanitization, AES-256 encryption, and mechanisms to prevent unauthorized data modification. This shift reflects a wider trend towards integrating security directly into hardware from the design stage. This matters because robust hardware-level security is becoming crucial for protecting valuable AI data in critical systems.