Data Protection
6 reportsThe evidence base around Data Protection is examined through approval pathway and enforcement action, with separate attention to legal authority. Conclusions concerning approval pathway remain tied to post-implementation evidence, statutory text, and transparent methods; interpretation remains cautious because legal authority and scientific evidence may address different questions.
Sitehop Deploys Hardware-Based Post-Quantum Security for Defense Networks
Sitehop has introduced a hardware-rooted post-quantum cryptography platform designed to retrofit legacy operational technology networks in the UK and Five Eyes defense infrastructure, aiming to address vulnerabilities to quantum and AI-enabled attacks without requiring full system replacement
Eclypses and Sterling Integrate Post-Quantum Cryptography for US Agencies
Eclypses and Sterling are deploying a FIPS 140-3 validated, quantum-resistant cryptographic platform for US federal agencies, aiming to address harvest-now-decrypt-later threats and meet upcoming post-quantum migration deadlines
QuProtect R3 Added to GSA Schedule for Federal Crypto Migration
QuSecure's QuProtect R3 platform is now available through Carahsoft's GSA Schedule, offering U.S. federal agencies a route to upgrade cryptographic systems in line with post-quantum security mandates and migration deadlines
EY Installs On-Site Quantum Computer to Meet Data Sovereignty Needs
Ernst & Young (EY) has deployed an on-premises quantum computer in Canada, aiming to process sensitive enterprise workloads while addressing regulatory and data sovereignty requirements for sectors including finance and healthcare
Public Claude AI Chats Indexed by Google, Exposing Sensitive Data
A technical lapse allowed Google to index publicly shared Claude AI conversations, making sensitive user data-including medical and business information-searchable until the links were removed from results
Medical AI Models Face Higher Patient Privacy Risks Than Expected
A new peer-reviewed study finds that diagnostic medical AI models can leak sensitive patient information at rates far above previous estimates, raising concerns about privacy, data security, and the need for robust technical safeguards in clinical machine learning.