Microsoft SDL: Evolving security practices for an AI-powered world

As AI reshapes the world, organizations encounter unprecedented risks, and security leaders take on new responsibilities. Microsoft’s Secure Development Lifecycle SDL is expanding to address AI-specific security concerns in addition to the traditional software security areas that it has...

CISA, Australia, and Partners Author Joint Guidance on Securely Integrating Artificial Intelligence in Operational Technology

CISA and the Australian Signals Directorate’s Australian Cyber Security Centre, in collaboration with federal and international partners, have released new cybersecurity guidance: Principles for the Secure Integration of Artificial Intelligence in Operational Technology. This guidance aims to hel...

Differential Privacy in Machine Learning: from Symbolic AI to LLMs

Machine learning models should not reveal particular information that is not otherwise accessible. Differential privacy provides a formal framework to mitigate privacy risks by ensuring that the inclusion or exclusion of any single data point does not significantly alter the output of an algorith...

Enabling Secure AI Inference: Trend Cybertron Leverages NVIDIA Universal LLM NIM Microservices

Learn how Trend's Cybertron has been harnessing the power of NVIDIA Universal LLM NIM Microservices...

CISA and UK NCSC Unveil Joint Guidelines for Secure AI System Development

Today, in a landmark collaboration, the U.S. Cybersecurity and Infrastructure Security Agency CISA and the UK National Cyber Security Centre NCSC are proud to announce the release of the Guidelines for Secure AI System Developmentlink is external. Co-sealed by 23 domestic and international...