Zero trust is a security model to help secure IT systems and environments. The core principle of this model is to never trust and always verify. It means never trusting devices by default, even those connected to a managed network or previously verified devices. Modern enterprise environments include networks consisting of numerous interconnected segments, services, and infrastructure, with connections to and from remote cloud environments, mobile devices, and Internet of Things (IoT) devices.

The Software Development Lifecycle (SDLC) framework defines the entire process required to plan, design, build, release, maintain and update software applications, including the final stages of replacing and decommissioning an application when needed. A Secure SDLC (SSDC) builds on this process, integrating security at all stages of the lifecycle. When migrating to DevSecOps (collaboration between Development, Security, and Operations teams), teams typically implement an SSDLC.

IT services are a critical backbone to the operations and functioning of most every business and organization. As more and more IT departments have embraced the need for good governance, this has driven greater transparency. From the perspective of IT service management, this has manifested itself as much greater openness when communicating about IT service availability.

SIGNL4 supports the remote execution of automated tasks or workflows in IT or IoT systems using Remote Actions. These remote actions offer a wide range of applications. You can execute remote actions in response to an alert to trigger some kind of remediation action. But there are many more possible use cases. This article provides some examples and ideas about what is possible.

A tool that aids in automating the software development process is called DevOps Tool. It largely concentrates on interaction and cooperation between experts in product management, software development, and operations. A DevOps solution also enables teams to automate the majority of software development procedures including build, conflict management, dependency management, deployment, etc. and lessens human labour.

People make mistakes, technology breaks down, and processes aren’t infallible. But, when incidents happen, what can we do about it? What can we learn? As with all things, learning isn’t a binary action, it’s a process. And, when an incident occurs, organizations typically conduct a post-mortem analysis and generate a post-incident review to uncover what went wrong and why.

When things go wrong, we try to learn for the next time. Every incident should be a learning opportunity to make your system more reliable for the future. Luckily with Blameless Reliability Insights, you can see patterns in incidents at a glance, right out of the box. In fact, the ability to tag incidents makes reliability data even more helpful by allowing you to collect granular details about reliability, especially as they pertain to your unique business needs. ‍

An incident has been declared and your runbook has fired. Everyone is gathered in your Slack channel, the tickets are opened, and roles are assigned. Now what? This is when most teams manually update status pages and kickoff investigation streams using a patchwork of tribal knowledge and supporting playbook documents.

Reliability and chaos might seem like opposite ideas. But, as Netflix learned in 2010, introducing a bit of chaos—and carefully measuring the results of that chaos—can be a great recipe for reliability. Although most software is created in a tightly controlled environment and carefully tested before release, the production environment is harsher and much less controlled.