The rapid evolution and enterprise adoption of AI has motivated bad actors to target these systems with greater frequency and sophistication. Many security leaders recognize the importance and urgency of AI security, but don’t yet have processes in place to effectively manage and mitigate emerging AI risks with comprehensive coverage of the entire adversarial AI threat … [Read more...] about Cisco Co-Authors Update to NIST Adversarial Machine Learning Taxonomy
Adversarial
New U.S. DoJ Rule Halts Bulk Data Transfers to Adversarial Nations to Protect Privacy
Dec 31, 2024Ravie LakshmananData Security / Privacy The U.S. Department of Justice (DoJ) has issued a final rule carrying out Executive Order (EO) 14117, which prevents mass transfer of citizens' personal data to countries of concern such as China (including Hong Kong and Macau), Cuba, Iran, North Korea, Russia, and Venezuela. "This final rule is a crucial step forward in … [Read more...] about New U.S. DoJ Rule Halts Bulk Data Transfers to Adversarial Nations to Protect Privacy
New Framework Released to Protect Machine Learning Systems From Adversarial Attacks
Microsoft, in collaboration with MITRE, IBM, NVIDIA, and Bosch, has released a new open framework that aims to help security analysts detect, respond to, and remediate adversarial attacks against machine learning (ML) systems. Called the Adversarial ML Threat Matrix, the initiative is an attempt to organize the different techniques employed by malicious adversaries in … [Read more...] about New Framework Released to Protect Machine Learning Systems From Adversarial Attacks
Adversarial use of current events as lures
By Nick Biasini. The goal of malicious activity is to compromise the system to install some unauthorized software. Increasingly that goal is tied to one thing: the user. Over the past several years, we as an industry improved exploit mitigation and the value of working exploits has increased accordingly. Together, these changes have had an impact on the threat landscape. We … [Read more...] about Adversarial use of current events as lures