The first annual cloud-native threat report from Sysdig explores some of the year’s most important security topics in the cloud. As the use of containers and cloud services keeps growing, threat actors are increasingly turning their attention to this new attack surface. The cloud offers unique opportunities for threat actors due to the inherent scalability and complexity of cloud resources.

You’ve got a problem to solve and turned to Google Cloud Platform and follow GCP security best practices to build and host your solution. You create your account and are all set to brew some coffee and sit down at your workstation to architect, code, build, and deploy. Except… you aren’t. There are many knobs you must tweak and practices to put into action if you want your solution to be operative, secure, reliable, performant, and cost effective.

Scanning a container image for vulnerabilities or bad practices on your GitHub Actions using Sysdig Secure is a straightforward process. This article demonstrates a step-by-step example of how to do it. The following proof of content showcased how to leverage the sysdig-cli-scanner with GitHub Actions. Although possible, it is not officially supported by Sysdig, so we recommend checking the documentation to adapt these steps to your environment.

TeamTNT is a prevalent threat actor who has been targeting cloud and virtual environments such as Kubernetes and Docker since at least late 2019. This threat actor is financially motivated, focusing their efforts on stealing credentials and cryptomining. In 2020, we analyzed their use of Weave Scope on an unsecured Docker API endpoint exposed to the internet. In December 2021, we attributed an attack to TeamTNT in which they targeted a vulnerable WordPress pod to steal AWS credentials.

Scanning a container image for vulnerabilities or bad practices in your Azure Pipelines using Sysdig Secure is a straightforward process. This article demonstrates a step by step example on how to do it. The following proof of content showcased how to leverage the sysdig-cli-scanner in Azure Pipelines. Although possible, it is not officially supported by Sysdig, so we recommend checking the documentation to adapt these steps to your environment.

If you work in Security or Operations, you are surely familiar with the concept of “alert fatigue.” Alert fatigue Syndrome is the feeling of becoming desensitized to alerts, causing you to potentially ignore or minimize risks and harming your capability to respond adequately to potential security threats.

Kubernetes Security Posture Management or KSPM refers to the security state and capabilities in place to manage the defense of the Kubernetes clusters and the workloads running on top of it. It tells us how well those capabilities can predict, prevent and respond to cyber threats in relation to Kubernetes. If that definition sounds familiar to you, it is because it is the common definition of Security Posture, but focused on Kubernetes Security.

Infrastructure as Code (IaC) is a powerful mechanism to manage your infrastructure, but with great power comes great responsibility. If your IaC files have security problems (for example, a misconfigured permission because of a typo), this will be propagated along your CI/CD pipeline until it is hopefully discovered at runtime, where most of the security issues are scanned or found. What if you can fix potential security issues in your infrastructure at the source?

AWS Security Groups (and Network ACLs and VPCs) are some of the fundamental building blocks of security in your cloud environment. They are similar to firewalls but are not the same thing. You have to understand this topic very well before you begin building in the cloud, because there are some subtle differences in how they are used, and you need to follow best practices. You should know your public cloud provider is contractually bound to honor its side of a shared responsibility model.

Contrary to what you might have read on the Internet, machine learning (ML) is not magic pixie dust. It’s a broad collection of statistical techniques that allows us to train a computer to estimate an answer to a question even when we haven’t explicitly coded the correct answer into the program.