Welcome to CloudSec Academy, your guide to navigating the alphabet soup of cloud security acronyms and industry jargon. Cut through the noise with clear, concise, and expertly crafted content covering fundamentals to best practices.
Our goal with this article is to share the best practices for running complex AI tasks on Kubernetes. We'll talk about scaling, scheduling, security, resource management, and other elements that matter to seasoned platform engineers and folks just stepping into machine learning in Kubernetes.
Containerization encapsulates an application and its dependencies into a container image, facilitating consistent execution across any host operating system supporting a container engine.
Cloud workload security protects workloads as they move across cloud environments through monitoring, access controls, encryption, and segmentation.
A Kubernetes cluster consists of a group of node machines designed to run applications within containers.
Helm Charts streamline the deployment of applications by providing a packaging format that includes all necessary Kubernetes resources.
A container engine is a software tool that automates the process of running applications in isolated, lightweight environments called containers.
eBPF provides deep visibility into network traffic and application performance while maintaining safety and efficiency by executing custom code in response to the kernel at runtime.
Kubernetes namespaces divide a given cluster into virtual clusters, helping to separate and manage resources while still keeping them within the same physical cluster. By segregating workloads and applying policies per namespace, you can create boundaries that keep your multi-tenant environments safe and organized.
Understanding the nuances of Linux containers is crucial for building robust, secure applications. This blog post provides insights into the practical implementation of containers, focusing on both their strengths and potential pitfalls.
7 essential best practices that every organization should start with
Docker containers leverage the Docker Engine (a platform built on top of Linux containers) to simplify the software development process.
Kubernetes runtime security refers to the measures and practices implemented to protect Kubernetes clusters and the applications running within them during their operational phase.
In Kubernetes, a security context defines privilege and access control settings for a Pod or Container. It allows you to specify security configurations such as user and group IDs, filesystem permissions, and capabilities.
Container monitoring is the process of collecting, analyzing, and reporting metrics and data related to the performance and health of containerized applications and their hosting environments.
Kubernetes role-based access control (RBAC) serves as a foundational security layer within Kubernetes. It is essential for regulating access to the K8s API and its resources, allowing organizations to define user roles with specific permissions to effectively control who can see or interact with what resources within a cluster.
A cloud workload protection platform (CWPP) is a security solution that provides continuous threat monitoring and protection for cloud workloads across different types of cloud environments.
Container runtime security is the combination of measures and technology implemented to protect containerized applications at the runtime stage.
Container orchestration involves organizing groups of containers that make up an application, managing their deployment, scaling, networking, and their availability to ensure they're running optimally.
The primary function of admission controllers is the enforcement of custom policies on incoming requests, ensuring that only valid and compliant API requests are executed.
At their core, containers encapsulate the application code and runtime, system tools, dependencies, and settings that enable it to operate in the same way across multiple environments.
A Kubernetes secret is an object in the Kubernetes ecosystem that contains sensitive information (think keys, passwords, and tokens)
In a nutshell, containers and virtual machines (VMs) are two inherently different approaches to packaging and deploying applications/services in isolated environments.
Kubernetes as a service (KaaS) is a model in which hyperscalers like AWS, GCP, and Azure allow you to quickly and easily start a Kubernetes cluster and begin deploying workloads on it instantly.
We cover the top container security tools across 7 common use cases, including image scanning, compliance, secrets management, and runtime security.
Kubernetes monitoring involves collecting, analyzing, and acting on performance data and metrics across your clusters.
