42 articles tagged with #data-protection. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Commvault has launched AI Protect, a governance solution that provides rollback capabilities for autonomous AI agents operating in cloud environments. The platform addresses critical risks posed by AI systems that can independently delete files, access databases, modify infrastructure, and alter security policies without adequate oversight or recovery mechanisms.
Researchers studying 469 Canadian youth aged 16-24 developed a negotiation-based framework to understand privacy decision-making with smart voice assistants, introducing two tension indices (RBTI and CATI) that measure competing risk-benefit and control-acceptance pressures. The study reveals that frequent SVA users exhibit benefit-dominant profiles and accept convenience trade-offs, suggesting the privacy paradox reflects negotiation rather than inconsistency.
Researchers propose AdaProb, a machine unlearning method that enables trained AI models to efficiently forget specific data while preserving privacy and complying with regulations like GDPR. The approach uses adaptive probability distributions and demonstrates 20% improvement in forgetting effectiveness with 50% less computational overhead compared to existing methods.
Researchers propose a new framework for 'selective forgetting' in Large Reasoning Models (LRMs) that can remove sensitive information from AI training data while preserving general reasoning capabilities. The method uses retrieval-augmented generation to identify and replace problematic reasoning segments with benign placeholders, addressing privacy and copyright concerns in AI systems.
Researchers developed a framework to assess public summaries of AI training data required by EU's AI Act Article 53(1)(d), evaluating transparency and usefulness for stakeholder rights enforcement. The study analyzed 5 public summaries from GPAI model providers as of January 2026, creating guidelines for compliance and a public resource website.
The article discusses ChatGPT's defensive mechanisms against prompt injection attacks and social engineering attempts. It focuses on how the AI system constrains risky actions and protects sensitive data within agent workflows to maintain security and reliability.
Researchers propose Talaria, a new confidential inference framework that protects client data privacy when using cloud-hosted Large Language Models. The system partitions LLM operations between client-controlled environments and cloud GPUs, reducing token reconstruction attacks from 97.5% to 1.34% accuracy while maintaining model performance.
Researchers have developed AloePri, the first privacy-preserving LLM inference method designed for industrial applications. The system uses collaborative obfuscation to protect input/output data while maintaining 96.5-100% accuracy and resisting state-of-the-art attacks, successfully tested on a 671B parameter model.
A study of 26 young Canadians reveals that smart voice assistants' complex privacy controls and lack of transparency discourage privacy-protective behaviors among youth. Researchers propose design improvements including unified privacy hubs, plain-language data labels, and clearer retention policies to empower young users while maintaining convenience.
A research study analyzed privacy and usability trade-offs in AI smart devices (Google Home, Alexa, Siri) used by youth, finding that Google Home scored highest for usability while Siri led in regulatory compliance. The study revealed that while youth feel capable of managing their data, technical complexity and unclear policies limit their privacy control.
OpenAI introduces new security features for ChatGPT including Lockdown Mode and Elevated Risk labels to help organizations protect against prompt injection attacks and AI-driven data exfiltration. These enterprise-focused security enhancements aim to address growing concerns about AI systems being exploited for malicious data access.
OpenAI has implemented safeguards to protect user data when AI agents interact with external links, addressing potential security vulnerabilities. The measures focus on preventing URL-based data exfiltration and prompt injection attacks that could compromise user information.
The article discusses a new differentially private framework designed to analyze AI chatbot usage patterns while protecting user privacy. This approach allows researchers to gain valuable insights into how users interact with AI systems without compromising individual data security.
The article discusses methods for running privacy-preserving machine learning inferences on Hugging Face endpoints. This technology allows users to perform AI model computations while protecting sensitive input data from being exposed to the service provider.
Hugging Face has partnered with Wiz Research to enhance AI security measures. This collaboration aims to improve security protocols and protect AI models and datasets on the Hugging Face platform.
Hugging Face has been selected to participate in the French Data Protection Agency's (CNIL) enhanced support program. This program provides regulatory guidance and support to help companies navigate data protection compliance requirements in France.
The article reviews nine privacy-focused AI tools as alternatives to Big Tech AI platforms that extensively collect user data. It evaluates different AI tools based on various threat models to help users choose options that better protect their privacy.
