If we’ve learned one thing during the COVID-19 pandemic, it’s that digital-native business models are essential to survival. That means cloud adoption is more important than ever before. Research shows that the public cloud market is expected to grow to $308.5 billion in 2021, an increase of 16% from 2020.

But while the cloud holds incredible potential, we have an outstanding security issue to address. That issue is cloud misconfigurations. Through 2026, 99% of cloud security failures will be the responsibility of the customer, quantifying the need for security professionals to turn their attention to security hygiene issues like cloud misconfigurations.

Digging Into the Cloud Misconfiguration Problem

To get to the root of the problem, we must fully understand the risk of cloud misconfigurations and how they differ from more traditional vulnerabilities. While traditional vulnerabilities can be patched, cloud misconfigurations can create vulnerabilities in otherwise secure applications and infrastructure. Imagine cloud infrastructure like a highway system and cloud misconfigurations as road hazards. They can be incredibly dangerous and lead to accidents.

As a specific example of cloud misconfigurations, let’s look at identity and access management (IAM). Poorly configured IAM, such as misconfigured roles or a lack of multi-factor authentication (MFA), can lead to compromised administrative accounts. If a threat actor hijacks a legitimate administrator account, they potentially can take full control of an entire cloud environment. Because IAM sits above the cloud infrastructure layer and all workloads and data within, once it is exploited a threat actor will often be able to circumvent your other security barriers, such as network segmentation, leaving you defenseless.

Cloud Misconfigurations Can Easily Scale

Where misconfiguration risk dramatically increases is through the adoption of cloud-native applications and practices like infrastructure as code (IaC) templates used by DevOps teams. These templates offer teams greater speed and scale for building and managing applications, but the downside is that misconfigurations can be unknowingly replicated from development environments to production environments (where sensitive data is stored) at greater velocity.

In short, the biggest risk to organizations right now is scaling these misconfigurations through the cloud. As the cloud grows in adoption and scale, we’re witnessing these misconfigurations scale right alongside it. Where before, a misconfiguration might have been limited to a siloed application or environment, today, that same misconfiguration can impact the entire organization without checks in place to catch it. Worse, many cloud storage buckets have logging disabled, meaning once threat actors are able to identify a misconfiguration and access an internal cloud bucket, an organization won’t even be able to see what data was accessed.

Understanding the Shared Responsibility Model

This all points to a higher-level, hygiene-related issue to keep in mind when securing cloud environments: responsibility. Particularly, organizations struggle to understand the shared responsibility model and how it applies within their own organizations. While some may fail to delineate between cloud provider responsibilities and their own, the major issue at stake is the shared responsibility between various internal teams that often goes undiscussed and undocumented until there is a security event.

When thinking about software-as-a-service (SaaS), to a much lesser degree the responsibility falls on consumers. However, when considering infrastructure- and platform-as-a-service (IaaS/PaaS) and all the moving parts such as network, user credentials, resource configurations, workloads, identity configurations and more, cloud consumers become responsible for much more. A key consideration to note is that one can never outsource accountability, no matter which cloud model is used. To put this more simply, if one puts data in any cloud provider, they are still accountable for that data.

Enabling a Holistic Cloud Security Strategy

When advising security and business leaders on how they can better secure their cloud environments and applications, I highlight what my team calls “The Big Cloud 5,” a set of recommendations developed to help organizations adopt a holistic cloud security model that accounts for proper security hygiene and shared responsibility.

Gain awareness and deep cloud visibility
The very first step to ensuring cloud security is understanding how teams are using cloud technologies, leveraging shadow IT and cloud provider APIs. This allows you to get situational awareness and make informed decisions today as well as in the future. This is not a one-time event, but something you’ll need to do continuously.
Set guardrails to automatically prevent the most serious cloud misconfigurations
Drawing lines in the sand around the most offensive (and potentially destructive) misconfigurations that should never exist in an environment is key to automating protection in the cloud. This will help keep templates and practices controlled, so that poor hygiene doesn’t inadvertently take root and spread. Think of this as your “dirty dozen.” What configurations should never exist in your cloud environments?
Standards are the precursor to automation
One can’t automate what hasn’t been standardized, and while there aren’t widely accepted security standards yet, key stakeholders in an organization must be in agreement about how to secure cloud infrastructures.
Train and hire security engineers who code
To fully leverage APIs, security teams must have engineers who know how to code and automate security processes. An assessment of skills that exist across your security team (e.g., knowledge of coding in the likes of Python or Ruby) can point to areas in training and hiring that need further investment.
Embed security in the development pipeline
Map out who, what, when, where and how your organization pushes code into the cloud. Once mapped, identify the least disruptive insertion points for security processes and tools, so that they can exist in as much of the development pipeline as possible.
If you feel your organization is a step behind on proper cloud security hygiene, know that you’re not alone. Earlier in 2021, the Cybersecurity and Infrastructure Security Agency (CISA) released a report highlighting the importance of strengthening security configurations based on the uptick in successful cloud attacks, often attributed to poor hygiene and the mixed use of computing devices in the remote work environment. While this is a growing problem, it is also a reversible trend. Best practices, cyber hygiene and a shared responsibility model can help companies safely and securely migrate to the cloud.

Understanding the underlying basics of Cloud-Native model in IT domain
Cloud has become an essential part of the IT framework. It acts as an accelerator for digital transformation, solves the data woes and enables quicker data processing with better ROI. It also supports the remote work culture that has become a significant part for enterprises amid the pandemic. As per an IBM study, a rapid shift to cloud over the next three years will drive enterprises to move 75 percent of existing non-cloud apps to cloud environments. Today, companies are racing to cloud-native to deliver innovation at scale and lower cost, as a key part of the digital transformation strategy. This is because, having cloud-based applications has help businesses augment their performance, agility, efficiency, scalability while mitigating the shortcomings of internally managed applications and servers.

Cloud-native is an approach to building and running applications that exploits the advantages of the cloud computing delivery model. The Cloud Native Computing Foundation states that these technologies empower organizations to build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds. CHCF also mentions that this form of computing uses an open-source software stack to be:

1. Containerized, where each part (applications, processes) is packaged in its own container. This facilitates reproducibility, transparency, and resource isolation from the underlying infrastructure. Further, containers also provide portability and guarantee consistency across environments.

2. Dynamically orchestrated where containers are actively scheduled and managed to optimize resource utilization. Common orchestrated tasks include scheduling, affinity/anti-affinity, detection of failures and health monitoring, networking, scaling, handling failover, service discovery and coordination of incremental upgrades with zero downtime deployment. There are multiple open tools available, e.g. Docker, Kubernetes, Amazon ECS, Mesos, that provide frameworks for handling the critical task of container orchestration.

3. Microservices-oriented, where applications are segmented into micro-services to increase the overall agility, functionality and maintainability of applications. Here, each micro-services has a well-defined boundary and API, and gets developed and operated by a relatively small team. They communicate with others using standard communication protocols such as HTTP/HTTPS, WebSockets, or AMQP.

To develop micro-services and manage it in different cloud environments independent of each other, we need a cloud-native development framework. This comprises of DevOps, agile methodology, microservices, cloud platforms, containers like Kubernetes and Docker, and continuous delivery. Kubernetes is an open-source system that incorporates functionality such as automation, integration, and self-service. It presents a unified environment for cloud-native development that enables teams to manage and modernize applications efficiently.

Next, an IT team must avoid silos in their quest to cloud progression. This is where DevOps can help all stakeholders to stay aligned to one mission and work continuously to achieve it. It also allows collaboration between IT Ops and Dev Teams. Here, the DevOps must have processes like approaches like minimum viable product (MVP) development, multivariate testing, rapid iteration, and working closely across organizational boundaries. This also involves automation of platform provisioning and application deployment via the practice of Infrastructure as Code or IaC.

Why do we need it?
Cloud-native helps IT by removing constraints and shortening the path to business value. It helps businesses automatically log into what’s going on and gives nice metrics, colorful dashboards and automated notifications. Besides, it improves the quality of IT service by continuous optimization due to better detection of problems and areas for improvement. It also provides real-time notifications.

Apps developed on cloud-native platforms have a significant advantage over on-premises ones. For instance, cloud-native apps are always current, and up to date, whereas, latter needs updates that are delivered on a subscription basis by the vendor, and require downtime while the update is installed. This is because the DevOps framework of cloud-native development help is making changes, and updates constant and iterative, promote a culture of seamless and continuous improvement. Further, cloud-native apps are modular, unlike monoliths architecture of on-premises apps. Next, cloud-native apps have no problem working in a virtualized space and sharing resources with other apps. It is important to remember that shifting on-premises apps to the cloud can result in failures and unnecessary challenges. Hence, it is crucial to regularly build new applications and update existing applications while moving to the cloud. Else, one can dismantle existing monoliths and refactor them using cloud-native principles from the ground up.

The Linux Foundation Releases Jenkins Essentials Course (LFS267)

The Linux Foundation, in collaboration with the Continuous Delivery Foundation (CDF), announced the availability of LFS267 – Jenkins Essentials, a new training course targeting DevOps engineers, Quality Assurance personnel, Site Reliability Engineers (SREs), software developers, and architects who want to deepen their expertise with Jenkins for CI/CD workflows.

Why Jenkins Still Matters

Jenkins is an open-source automation server that offers hundreds of plugins supporting the building, deploying, and automation of virtually any software project. First released in 2004, Jenkins has maintained its position as one of the most widely adopted CI servers in the world. Its extensibility and large community make it a common choice for teams building CI/CD pipelines at scale.

What You Will Learn in LFS267

The course covers a broad range of practical, production-relevant Jenkins skills. By the end of the course, students will be able to:

• Deploy Jenkins on Kubernetes — set up and manage Jenkins within a Kubernetes cluster for containerized workloads.
• Automate Jenkins installation with IaC — apply Infrastructure-as-Code principles to provision and configure Jenkins reliably.
• Configure a highly available Jenkins server — architect Jenkins for resilience and uptime in production environments.
• Scale with static and dynamic build agents — expand Jenkins infrastructure horizontally to handle varying build loads.
• Automate routine tasks via CLI and REST API — script and automate common Jenkins administration and job management tasks.
• Implement fine-grained security controls — secure Jenkins jobs and resources with role-based access and security policies.
• Set up effective CI/CD feedback loops — integrate notifications and reporting so teams get actionable build feedback quickly.
• Build complex multi-branch pipelines — create pipelines that mirror real-world branching strategies and release flows.
• Automate pull request workflows with GitOps — integrate Jenkins with Git repositories to trigger and validate PRs automatically.

These competencies map directly to SRE, DevOps engineer, and release engineer roles.

Prerequisites

Before enrolling in LFS267, students should have a working familiarity with Jenkins, Kubernetes, and Docker. Those who are new to any of these technologies can first complete free introductory courses from The Linux Foundation:

• Introduction to Jenkins (LFS167x)
• Introduction to Cloud Infrastructure Technologies
• Introduction to Kubernetes

About the Course Authors

LFS267 was developed by Himanshu Gautam and Deepika Gautam, co-founders of Aplima Inc., a CI/CD solutions provider.

Himanshu Gautam is an author, trainer, speaker, and software engineer with over 20 years of industry experience. He focuses on cloud-native architecture and DevOps practices, and has also authored the CI/CD with Jenkins X course (LFS268) for the Linux Foundation.

Deepika Gautam is an author, trainer, speaker, and DevOps evangelist with nearly two decades of software industry experience. She specializes in implementing DevOps toolchains across multi-cloud environments and authored the Introduction to Jenkins course (LFS167x).

About the Continuous Delivery Foundation

The Continuous Delivery Foundation (CDF) is a vendor-neutral organization under the Linux Foundation that works to establish best practices for software delivery automation, promote adoption of CD tooling, and encourage collaboration across the ecosystem. The CDF hosts several prominent open-source projects including Jenkins, Jenkins X, Tekton, and Spinnaker.

About the Linux Foundation

Founded in 2000, the Linux Foundation supports open-source collaboration across software, standards, data, and hardware. It is home to critical infrastructure projects including Linux, Kubernetes, and Node.js, and is supported by more than 1,000 member organizations worldwide.

Linux Foundation Releases Jenkins Essentials Training Course (LFS267)

The Linux Foundation, in collaboration with the Continuous Delivery Foundation (CDF), announced the availability of a new instructor-developed training course: LFS267 – Jenkins Essentials. The course targets DevOps engineers, Quality Assurance (QA) professionals, Site Reliability Engineers (SREs), software developers, and architects who want to build practical expertise with Jenkins for continuous integration and continuous delivery (CI/CD) workflows.

Why Jenkins Still Matters

Jenkins is an open-source automation server that has been a cornerstone of software delivery pipelines since its inception in 2004. With hundreds of plugins covering build, test, deploy, and notification workflows, it remains one of the most widely adopted CI servers in the industry. As organizations migrate workloads to Kubernetes and adopt GitOps practices, the need to operate Jenkins at scale—and to integrate it cleanly into cloud-native environments—has grown significantly.

What You Will Learn in LFS267

The course is structured to take learners from foundational Jenkins operations through advanced, production-grade configurations. Key topics include:

• Running Jenkins on Kubernetes – deploying and managing Jenkins controller and agents inside a Kubernetes cluster.
• Infrastructure-as-Code (IaC) automation – automating Jenkins installation and configuration using IaC principles to eliminate manual, error-prone setup.
• High availability – architecting a highly available Jenkins environment to minimize downtime.
• Scaling with static and dynamic agents – leveraging both pre-provisioned static build agents and on-demand dynamic agents to match workload requirements.
• CLI and REST API automation – scripting routine Jenkins administrative tasks to reduce toil.
• Fine-grained security controls – implementing role-based access and job-level security to protect CI/CD pipelines.
• Effective CI/CD feedback loops – configuring notifications and reporting so teams get actionable build and test feedback quickly.
• Multi-branch pipelines – building complex, real-world pipelines that handle feature branches, release branches, and hotfixes simultaneously.
• GitOps-driven pull request workflows – automating PR validation and merge gates using GitOps principles.

Together, these skills map directly to SRE, DevOps engineer, and release engineer job responsibilities.

Prerequisites

Students should arrive with a working understanding of Jenkins, Kubernetes, and Docker before enrolling. If you need to fill gaps in any of those areas, the Linux Foundation offers free introductory courses that can serve as on-ramps:

• Introduction to Jenkins (LFS167x) – free foundational Jenkins course.
• Introduction to Cloud Infrastructure Technologies – covers core cloud and container concepts.
• Introduction to Kubernetes – covers Kubernetes fundamentals needed to follow the cluster-deployment labs.

Course Authors

LFS267 was developed by Himanshu Gautam and Deepika Gautam, co-founders of Aplima Inc., a CI/CD solutions provider.

Himanshu brings more than 20 years of software engineering experience. He is a practitioner focused on cloud-native architecture and DevOps culture, and also authored the CI/CD with Jenkins X (LFS268) course for the Linux Foundation.

Deepika has nearly two decades of software industry experience specializing in DevOps toolchain implementation across multi-cloud environments. She authored the Introduction to Jenkins (LFS167x) course.

Pricing and Access

At the time of announcement, LFS267 was priced at $299, which includes unlimited access to all course content and hands-on labs for one year. The course was available to enroll immediately upon announcement.

Note: Pricing and availability may have changed since the original October 2020 announcement. Check the Linux Foundation Training catalog for current details.

About the Linux Foundation and the Continuous Delivery Foundation

Founded in 2000, the Linux Foundation is a nonprofit organization that hosts collaborative development of critical open-source infrastructure, including Linux, Kubernetes, and Node.js, and is supported by more than 1,000 member organizations.

The Continuous Delivery Foundation (CDF) is a vendor-neutral, Linux Foundation-hosted organization dedicated to improving software delivery through security and speed. The CDF stewards some of the fastest-growing CD projects, including Jenkins, Jenkins X, Tekton, and Spinnaker, and drives best-practice education and cross-project collaboration across the CD ecosystem.

Why Azure DevOps Skills Matter

Microsoft Azure is one of the most widely adopted cloud platforms in enterprise environments. For DevOps engineers, knowing how to deploy, test, monitor, and automate workloads on Azure is increasingly a baseline expectation rather than a differentiator. Building these skills also maps directly to Microsoft’s AZ-400: Designing and Implementing Microsoft DevOps Solutions certification exam, which validates expertise across the full DevOps lifecycle on Azure.

The following sections break down the three core competency areas that Azure DevOps engineers need to master.

1. Azure Infrastructure Operations

Modern cloud deployments depend on automated, repeatable infrastructure provisioning. Manual server configuration doesn’t scale and introduces human error — both of which undermine the uptime guarantees customers expect. Azure DevOps engineers must understand how to manage infrastructure programmatically rather than through the portal alone.

Key Skills in This Area

• DevOps lifecycle fundamentals: Understanding the relationship between planning, development, delivery, and operations in a continuous loop.
• Infrastructure as Code (IaC): Using tools such as Azure Resource Manager (ARM) templates, Bicep, or Terraform to define and version infrastructure declaratively.
• Cloud security best practices: Applying the principle of least privilege, managing secrets with Azure Key Vault, and enforcing network security groups and policies.
• Repeatable deployments: Building deployment pipelines that can recreate environments consistently across development, staging, and production.

The goal is infrastructure that behaves like code — reviewable, version-controlled, and testable before it ever touches production.

2. Agile Development with Azure

Delivering high-quality software quickly requires marrying Agile project management with automated CI/CD pipelines. In the Azure ecosystem, this typically means combining Azure Boards for work tracking with Azure Pipelines for build and release automation.

Continuous Integration with Python Tooling

A common CI workflow for Python-based projects on Azure involves the following stages in a pipeline:

1. Linting with pylint — catches syntax errors and enforces style standards before a single test runs.
2. Unit and integration testing with pytest — verifies business logic and catches regressions automatically on every commit.
3. Build artifact creation — packages the application for deployment after all checks pass.

Continuous Delivery with Azure Pipelines

Pairing IaC with Azure Pipelines enables end-to-end Continuous Delivery: infrastructure and application code are deployed together through the same automated workflow. This eliminates environment drift and reduces the time between a code commit and a production release.

# Example: minimal azure-pipelines.yml trigger for a Python app
trigger:
  - main

pool:
  vmImage: 'ubuntu-latest'

steps:
  - script: pip install pylint pytest
    displayName: 'Install dependencies'

  - script: pylint src/
    displayName: 'Lint with pylint'

  - script: pytest tests/
    displayName: 'Run tests with pytest'

The snippet above is illustrative. Real-world pipelines add caching, environment-specific variable groups, approval gates, and deployment stages.

3. Ensuring Quality Releases (QA and Monitoring)

Shipping to the cloud is not the end of the DevOps loop — it’s the beginning of the operations phase. Released applications must be continuously evaluated for performance, correctness, and reliability.

Testing Inside and Outside the Pipeline

Quality assurance for cloud applications typically spans two phases:

• In-pipeline testing: Tests that run as pipeline stages before or immediately after deployment — blocking a release if they fail.
• Post-deployment testing: Tests that run against the live environment to validate real-world behaviour, including load and end-to-end UI scenarios.

Common tooling includes:

• Selenium — browser-based UI testing, useful for validating web frontends such as eCommerce applications.
• Postman / Newman — API testing and contract validation against REST endpoints, runnable from the command line inside a pipeline.

Monitoring and Alerting with Azure-Native Tools

Once an application is live, observability is non-negotiable. Azure provides two key services for this:

• Azure Monitor: Collects metrics and logs from Azure resources, and triggers alerts based on thresholds you define — such as error rate spikes or latency increases.
• Azure Log Analytics: A query-driven workspace for analysing custom and platform log data using Kusto Query Language (KQL), enabling root-cause analysis and trend detection.

Building alerting rules and dashboards as part of the deployment process — rather than as an afterthought — is a hallmark of mature DevOps practice.

Mapping to the AZ-400 Certification

The three competency areas above align closely with the domains tested in Microsoft’s AZ-400 exam: DevOps transformation planning, source control, CI/CD pipeline implementation, dependency management, application infrastructure, and continuous feedback. Engineers who build practical experience in Azure infrastructure automation, pipeline development, and QA testing are well-positioned to sit the exam.

Review the official AZ-400 exam skills outline on Microsoft Learn for the current objectives before studying.

Summary

Azure DevOps engineering requires depth across three interconnected domains: infrastructure automation and security, Agile-driven CI/CD pipelines, and systematic quality assurance combined with production monitoring. Developing hands-on proficiency in tools like Azure Pipelines, ARM/Bicep/Terraform, Selenium, Postman, Azure Monitor, and Log Analytics builds the practical foundation needed for both day-to-day engineering work and the AZ-400 certification.

What Skills Does a DevOps Engineer Need?

The top priority in DevOps is to deliver high-value features frequently through cross-team collaboration. Achieving that goal requires a mix of interpersonal (“soft”) skills and technical (“hard”) skills. Whether you are already working in DevOps or preparing to move into the role, mapping your existing strengths against this framework is a useful starting point.

1. Communication and Collaboration

DevOps dissolves the traditional wall between development and operations by bringing small teams with different objectives together around a shared goal: fast, reliable releases. That model only works when communication flows freely.

A strong DevOps engineer communicates regularly with internal stakeholders — product managers, architects, QA leads — to stay aligned on roadmaps, blocking issues and release objectives. They also need to communicate clearly with customers when support issues arise.

Collaboration goes beyond being a good teammate. Effective DevOps engineers mentor colleagues on coding standards, tool selection and delivery best practices, and actively support the team throughout sprints and iterations.

2. Mastery of the DevOps Toolchain

A DevOps engineer must understand how a diverse set of tools integrates into a coherent delivery pipeline. Knowing what each tool does — and how it interacts with everything else — is as important as knowing how to configure it. The key categories include:

• Version control — tracking changes to code and configuration (e.g., Git)
• Continuous Integration (CI) servers — automating build and test on every commit (e.g., Jenkins, GitHub Actions, GitLab CI)
• Configuration management — maintaining consistent system state (e.g., Ansible, Chef, Puppet)
• Deployment automation — reliably promoting artefacts through environments
• Containers — packaging applications for portability and consistency (e.g., Docker)
• Infrastructure orchestration — managing containerised workloads at scale (e.g., Kubernetes)
• Monitoring and analytics — observing system health and user behaviour in production
• Testing and cloud quality tools — validating behaviour at every stage of the pipeline
• Network protocols — understanding how services communicate across environments

Your responsibility is to ensure these tools work together reliably in both pre-production and production stages. Knowing which tool depends on which, and in what order they fire, is essential for a stable delivery chain.

3. Programming Skills and Mature Coding Standards

DevOps engineers are not expected to be language specialists, but they do need to be comfortable working across multiple programming and scripting languages. Common examples include Python, Ruby, Bash, Java, JavaScript and PHP — the exact mix will depend on your organisation’s stack.

These skills serve two purposes:

1. Automating the pipeline — writing scripts and tools that eliminate manual handoffs between stages.
2. Integration quality — ensuring your code integrates cleanly with code produced by other teams, often in different environments and languages.

Test automation is a particularly important coding skill for DevOps engineers. Since CI/CD pipelines depend on automated tests to gate every build, the ability to write, maintain and debug test code in multiple languages is a real differentiator. Managing complex pipeline configurations in tools such as Jenkins requires discipline and a thorough end-to-end understanding of how changes propagate through the system.

4. Syncing with QA Teams

DevOps engineers do not work in isolation from quality assurance. To hit sprint goals and release dates consistently, they need continuous visibility into QA activities. Specifically, a DevOps engineer should:

• Understand what testing activities are happening and when
• Know the history of test results across the CI/CD cycle
• Be familiar with the frameworks and environments that QA teams own

With that knowledge, a DevOps engineer can identify which existing test automation frameworks can be reused for other tasks — such as reproducing production issues or validating specific environments — rather than rebuilding work from scratch.

Importantly, adopting DevOps does not mean automating everything. Manual testing continues to play a vital role even in fast-moving CI/CD environments. Test automation excels at validating the “happy path” repeatedly and reliably, but exploratory testing is necessary for:

• Discovering the alternative paths that real users take
• Testing scenarios that are too complex or too context-dependent to automate effectively
• Covering cases where the return on automation investment does not justify the effort

DevOps engineers who find the right balance between automated and manual testing — and who adjust that balance as the system evolves — put their products in the strongest position for continuous, reliable delivery.

5. Non-Functional Skills: Security, Monitoring and Infrastructure as Code

Beyond the functional pipeline, DevOps engineers need competency in several non-functional areas:

• Monitoring and observability — tracking application performance, error rates and infrastructure health in production, and acting on that data.
• Infrastructure as Code (IaC) — using tools such as Terraform or AWS CloudFormation to define and manage infrastructure programmatically, enabling reproducibility and automation.
• Cloud administration — understanding the platforms (AWS, Azure, GCP or others) on which services run.
• Security (DevSecOps) — integrating security practices directly into the pipeline. Because DevOps ships code frequently in small increments, security checks must be continuous rather than a gate at the end of a release cycle. DevOps engineers should take an active role in this, not treat security as someone else’s concern.

In larger teams, individual engineers may specialise in one or more of these areas. Even so, every DevOps engineer should have enough familiarity across all of them to collaborate effectively with specialists and to recognise when something is wrong.

6. Technical Support and Operational Ownership

A DevOps engineer is not just a pipeline builder — they own and operate the tools and environments the entire team depends on, and sometimes customer-facing capabilities too. That ownership comes with operational responsibilities:

• Real-time monitoring — maintaining visibility into system state at all times
• Alerting — ensuring that meaningful alerts fire when something degrades or fails, and that the right people are notified
• Incident response — troubleshooting and resolving issues quickly, whether through online support, runbooks or direct technical intervention

Keeping production systems available around the clock is a core operational expectation for most organisations running modern software. DevOps engineers are central to meeting that expectation.

Bottom Line

DevOps engineering is not a single discipline — it is a combination of communication, toolchain knowledge, programming, testing awareness, security sensibility and operational discipline. The engineers who thrive in this role are those who can hold the whole delivery system together while helping the people around them move faster and more confidently.

As organisations mature their DevOps practices, the role continues to expand — particularly into security, observability and platform engineering. Starting with the skills outlined here gives you a strong, durable foundation for that growth.