AI and agents
AI and machine learning help you identify threats sooner and respond more effectively. Learn how to safeguard your infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) resources across multicloud and hybrid environments.
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We developed a cloud-based machine learning system that, when queried by a device, intelligently predicts if it is at risk, then automatically issues a more aggressive blocking verdict to protect the device, thwarting an attacker’s next steps. -
Automating security assessments using Cloud Katana
Today, we are open-sourcing Cloud Katana, a cloud-native tool under development, to automate simulation steps on-demand in multi-cloud and hybrid cloud environments. -
Attack AI systems in Machine Learning Evasion Competition
Today, we are launching MLSEC.IO, a new machine learning security evasion competition as an educational effort for the AI and security communities to exercise their muscle to attack critical AI systems in a realistic setting. -
AI security risk assessment using Counterfit
Counterfit is a command-line tool for security professionals to red team AI systems and systematically scans for vulnerabilities as part of AI risk assessment. -
Cyberattacks against machine learning systems are more common than you think
Machine learning (ML) is making incredible transformations in critical areas such as finance, healthcare, and defense, impacting nearly every aspect of our lives. -
CISO Spotlight: How diversity of data (and people) defeats today’s cyber threats
This year, we have seen five significant security paradigm shifts in our industry. -
Security Unlocked—A new podcast exploring the people and AI that power Microsoft Security solutions
It’s hard to keep pace with all the changes happening in the world of cybersecurity. -
Best practices for defending Azure Virtual Machines
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. -
Seeing the big picture: Deep learning-based fusion of behavior signals for threat detection
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. -
Misconfigured Kubeflow workloads are a security risk
Azure Security Center monitors and defends thousands of Kubernetes clusters running on top of Azure Kubernetes Service. -
Secure the software development lifecycle with machine learning
A collaboration between data science and security produced a machine learning model that accurately identifies and classifies security bugs based solely on report names. -
Welcoming more women into cybersecurity: the power of mentorships
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.