AI, analytics push data-in-use protection up priority list

TechTarget reports that standard security leaves data vulnerable in system memory and CPUs[1], and that data-in-use protection deals with this by "keeping information encrypted while workloads execute."[1]The outlet says that among privacy-enhancing technologies, confidential computing has emerged as the primary way to address this processing‑stage risk.[1] TechTarget describes a trusted execution environment (TEE) as a secure area that runs code and processes data independently from the rest of the system[1]. It adds that even cloud administrators, host OSes and hypervisors do not have access to the data in a TEE.[1]According to TechTarget, rapidly evolving regulations are reshaping where organizations invest to secure AI and analytics workloads.[1] The outlet notes that the NIST Cybersecurity Framework 2.0 includes data-in-use protection within zero-trust security designs.[1]On compliance, TechTarget says confidential computing provides verifiable proof that sensitive workloads are processed in hardware-isolated environments[1]. It also reports that the processing records TEEs generate automatically reduce manual auditing work and improve audit verification on the use of sensitive data.[1]The outlet reports that healthcare records, financial transaction data, personally identifiable information and other regulated data often aren't used in AI and analytics initiatives due to processing risks.[1]TechTarget advises that before choosing a platform, data leaders should focus on proof points, regulatory alignment and how well it integrates with existing controls.[1]