AI-driven systems have become prime targets for sophisticated cyberattacks, exposing critical vulnerabilities across industries. As organizations increasingly embed AI and machine learning (ML) into ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Security leaders’ intentions aren’t matching up with their actions to ...

NIST’s National Cybersecurity Center of Excellence (NCCoE) has released a draft report on machine learning (ML) for public comment. A Taxonomy and Terminology of Adversarial Machine Learning (Draft ...

The final guidance for defending against adversarial machine learning offers specific solutions for different attacks, but warns current mitigation is still developing. NIST Cyber Defense The final ...

The National Institute of Standards and Technology (NIST) has published its final report on adversarial machine learning (AML), offering a comprehensive taxonomy and shared terminology to help ...

The Artificial Intelligence and Machine Learning (“AI/ML”) risk environment is in flux. One reason is that regulators are shifting from AI safety to AI innovation approaches, as a recent DataPhiles ...

Adversarial machine learning explained: How attackers disrupt AI and ML systems Threat actors have several ways to fool or exploit artificial intelligence and machine learning systems and models, but ...

Long gone are the days of only discovering the existence of cyber threats and deciding what to name each of them. Cyberthreats grow—not only in complexity but in frequency, and one of the things that ...

Member of the ICMAT, AXA-ICMAT Chair in Adversarial Risk Analysis and Member of the Spanish Royal Academy of Sciences, Instituto de Ciencias Matemáticas (ICMAT-CSIC) David Rios Insua has received ...