It’s hard to keep pace with all the changes happening in the world of cybersecurity. Security experts and leaders must continue learning (and unlearning) to stay ahead of the ever-evolving threat landscape. In fact, many of us are in this field because of our desire to continuously challenge ourselves and serve the greater good.
One of the things that our Detection and Response Team (DART) and Customer Service and Support (CSS) security teams see frequently during investigation of customer incidents are attacks on virtual machines from the internet. This is one area in the cloud security shared responsibility model where customer tenants are responsible for security.
Learn how we’re using deep learning to build a powerful, high-precision classification model for long sequences of wide-ranging signals occurring at different times.
Azure Security Center monitors and defends thousands of Kubernetes clusters running on top of Azure Kubernetes Service. In this blog, we’ll reveal a new campaign that was observed recently by ASC that targets Kubeflow, a machine learning toolkit for Kubernetes.
A collaboration between data science and security produced a machine learning model that accurately identifies and classifies security bugs based solely on report names.
I’ve long been a proponent to challenging traditional schools of thought—traditional cyber-norms—and encouraging our industry to get outside its comfort zones.
New innovations for built-in and cross-platform security that embrace AI.
Microsoft Insider Risk Management and Communication Compliance in Microsoft 365 help organizations address internal risks—such as IP theft or code of conduct policy violations.
Threat protection that changes our approach to attacks requires built-in intelligence that can understand how an attack got in, prevent its spread across domains, and automatically heal compromised assets.
On this episode of Afternoon Cyber Tea, Ann Johnson and Jane Frankland discuss chatbots, AI, and increasing the number of women in cybersecurity.
