Google Kubernetes Engine (GKE) provides many ways to help secure your workloads. Protecting workloads in GKE involves many layers of the stack, including the contents of your container image, the container runtime, the cluster network, and access to the cluster API server.
It’s best to take a layered approach to protecting your clusters and workloads. You can apply the principle of least privilege to the level of access provided to your users and your application. In each layer, there may be different tradeoffs that must be made that allow the right level of flexibility and security for your organization to securely deploy and maintain their workloads. For example, some security settings may be too constraining for certain types of applications or use cases to function without significant refactoring. In this talk, we will cover an overview of each layer of your infrastructure, and also the best practices for configuring its security feature
Usha is India’s first women Kaggle Grandmaster and she is ranked as top ten Data Scientists in India for the year 2020 by Analytics India Magazine. She is Data Science Global Ambassador for HP and NVIDIA. She organized NeuroAI (www.neuroai.in) which is India’s first-ever research symposium in the interface of Neuroscience and Data Science. She specializes in Probabilistic Graphical Models, Machine Learning, and Deep Learning. She has prepared curriculum for BITS Pilani’s masters in Data Science program (consumed by 20,000+ students ) and Upgrad’s PGP program in DS(consumed by 10,000+ students). She leads 8 communities across two cities Bangalore and Mysuru ( TensorFlow User Group(TFUG Mysuru), Google Developer Group (GDG Mysuru) , Women TechMakers Ambassador for Mysuru, Women in Data Science Ambassador (Mysuru) , Women in Machine Learning and Data Science (WiMLDS - Bangalore and Mysore) , Lean in Bangalore AI circle and AIMed Ambassador. Some of the above communities have grown to become the biggest chapters in India.