Operations | Monitoring | ITSM | DevOps | Cloud

If there’s one Go programming language feature that just doesn’t seem to catch a break when it comes to security, it’s the CFLAGS and LDFLAGS handling in cgo. This is a feature that lets parts of Go source code control the compiler and linker flags that are used to build that same code.

An Internal Developer Platform (IDP) is a powerful tool for providing self-service infrastructure for developers. It allows developers to manage application lifecycles without the constant need for IT team involvement, streamlining the process from development to production. With its widespread adoption, Kubernetes has become the standard for running application workloads across various organizations.

Managing security in the cloud and throughout hybrid environments is a challenge with high stakes — customer data, sensitive information, access privileges, and other cloud-based assets are all at risk when an organization uses the cloud. Let’s explore some common cloud-based security concerns and learn how to keep your cloud environment secure.

It’s one thing to say that reliability is a priority for your organization, and a whole other thing to make actual, demonstrable improvements in the availability of your applications. Sadly, it’s common for organizations to invest time, money, and effort into improving reliability only to barely nudge the needle on incidents and downtime. But there are hundreds of companies successfully improving their reliability posture—and doing it at enterprise scale.

Modern, distributed application architectures pose particular challenges when it comes to coordinating incident management. DevOps, SREs, and security teams—often spread out across separate locations and time zones, and equipped with limited knowledge of each other’s services—must work quickly to collaboratively triage, troubleshoot, and mitigate customer impact.

In the cloud-centric world of today, enterprises rely on highly available connectivity for access to public-facing Google Cloud apps, Google Workspace, or Google APIs. Some also need to access latency sensitive Secure Access Service Edge (SASE) solutions, combining security and networking services on one cloud platform.

AI-driven processes, including machine learning models and data processing, require significant computational resources which can lead to increased energy consumption and heightened operational costs. The complexity of these workloads, which often involve real-time data analysis and continuous model training, exacerbates the need for robust data center management.