Intrusion detection systems, long constrained by high false-positive rates and limited adaptability, are being re-engineered ...

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 ...

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 ...

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 ...

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 ...

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 ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The National Institute of Standards and Technology (NIST) has released an ...

AI is no longer an experimental capability or a back-office automation tool: it is becoming a core operational layer inside modern enterprises. The pace of adoption is breathtaking. By Amy Chang, AI ...

AI red teaming — the practice of simulating attacks to uncover vulnerabilities in AI systems — is emerging as a vital security strategy. Traditional red teaming focuses on simulating adversarial ...

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...