Are you interested in pursuing a career in DevOps? Do you want to enhance your skills and knowledge in this field? If so, getting a DevOps Foundation Certification can be a great way to validate your expertise and open up new opportunities. In this article, we will explore the steps you need to take to obtain the DevOps Foundation Certification and why it is worth pursuing.

What is DevOps Foundation Certification?

DevOps Foundation Certification is an introductory certification offered by the DevOps Institute that validates the basic understanding of core DevOps principles and practices, vocabulary, and culture. It covers all the theoretical concepts and benefits related to DevOps adoption.

The certification is designed for anyone who wants to learn about DevOps, including:

• Software developers
• IT operations professionals
• Business analysts
• Project managers
• Quality assurance engineers
• Anyone interested in learning about DevOps

Why DevOps Certification is important?

The DevOps Foundation Certification is a valuable credential for anyone who wants to learn about DevOps or advance their career in DevOps. It demonstrates that you have a basic understanding of the principles and practices of DevOps, and it can help you to get a job in DevOps or to advance your career in your current role.

The DevOps Foundation Training Course offered by DevOpsSchool equips individuals and teams with the essential mindset, tools, and techniques needed to accelerate software delivery and foster a culture of collaboration between development and operations. Throughout the course, participants dive into foundational concepts such as continuous integration and delivery (CI/CD), infrastructure as code (IaC), version control, test automation, and system monitoring. With a blend of instructor-led sessions, hands-on labs, and real-world scenarios, the training prepares developers, operations engineers, IT managers, and those new to DevOps to build and maintain efficient, resilient, and scalable delivery pipelines. Explore the full course details and register here: DevOps Foundation Training Course.

Here are some of the benefits of getting the DevOps Foundation Certification:

• Demonstrate your knowledge of DevOps: The certification shows that you have a basic understanding of the principles and practices of DevOps. This can be a valuable asset when you are applying for jobs or seeking promotions.
• Gain access to exclusive resources: The DevOps Institute offers a variety of resources to certified professionals, such as networking opportunities, training courses, and certification discounts.
• Stay up-to-date on the latest trends: The DevOps Institute regularly updates its certification exams to reflect the latest trends in DevOps. This ensures that you are always up-to-date on the latest knowledge and skills.
• Advance your career: The DevOps Foundation Certification is a great way to demonstrate your commitment to DevOps and to advance your career in the field. Many organizations now require their employees to have this certification.

Why should you get DevOps Foundation Certification?

There are many reasons why you should get the DevOps Foundation Certification. Here are a few of the most important ones:

• Demonstrate your knowledge of DevOps: The certification shows that you have a basic understanding of the principles and practices of DevOps. This can be a valuable asset when you are applying for jobs or seeking promotions.
• Gain access to exclusive resources: The DevOps Institute offers a variety of resources to certified professionals, such as networking opportunities, training courses, and certification discounts.
• Stay up-to-date on the latest trends: The DevOps Institute regularly updates its certification exams to reflect the latest trends in DevOps. This ensures that you are always up-to-date on the latest knowledge and skills.
• Advance your career: The DevOps Foundation Certification is a great way to demonstrate your commitment to DevOps and to advance your career in the field. Many organizations now require their employees to have this certification.
• Increase your salary: According to a recent survey, DevOps engineers earn an average salary of $110,000. With a DevOps Foundation Certification, you can expect to earn even more.
• Make a difference in the world: DevOps is a rapidly growing field that is changing the way software is developed and delivered. By getting certified in DevOps, you can help to make a difference in the world by creating better software that is more reliable and secure.

What are the tools need to learn for a strong DevOps Foundation?

These are just a few of the many tools that are essential for a strong DevOps foundation. By learning these tools, you will be well on your way to becoming a DevOps engineer.

In addition to the tools listed above, there are a few other skills that are essential for a strong DevOps foundation. These include:

• Agile development: Agile development is a methodology for software development that emphasizes iterative development and continuous feedback. This is essential for DevOps, as it allows teams to deliver software more quickly and reliably.
• Cloud computing: Cloud computing is the delivery of computing resources, such as servers, storage, and networking, over the internet. This is essential for DevOps, as it allows teams to deploy software to a variety of environments quickly and easily.
• Security: Security is a critical part of DevOps. Teams need to be aware of the security risks involved in deploying software and take steps to mitigate those risks.

How DevOpsSchool is best for DevOps Foundation Certification?

As of my last knowledge update in September 2021, I don’t have specific information about every organization or training provider in the DevOps field, including DevOpsSchool. However, I can offer you some general considerations to help you evaluate whether DevOpsSchool or any other training provider is a good fit for your DevOps Foundation certification journey:

• Reputation and Reviews: Research DevOpsSchool’s reputation within the DevOps community. Look for reviews, testimonials, and feedback from past students. Online platforms like review sites, forums, and social media can provide insights into the quality of their training.
• Course Content: Review the curriculum and course content they offer for the DevOps Foundation certification. Ensure that the topics covered align with the certification exam objectives and provide a comprehensive understanding of DevOps practices and principles.
• Trainers’ Expertise: Investigate the qualifications and experience of the trainers or instructors at DevOpsSchool. Qualified instructors with practical industry experience can enhance your learning experience.
• Learning Formats: Check if DevOpsSchool offers various learning formats, such as in-person classes, online instructor-led training, self-paced courses, or a combination of these. Choose the format that suits your learning style and schedule.
• Practice Materials: Look for the availability of practice exams, quizzes, labs, and hands-on exercises that help you apply what you’ve learned and prepare effectively for the certification exam.

NCover is a .NET code coverage tool. There are two non-related NCover products that do .NET code coverage. There is an open-source ncover which can be found on Sourceforge and a company called Encover, LLC. There has been additional development on both products since this 2004 reference. The company NCover, LLC was started when founder Peter Waldschmidt decided to commercialize the open-source tools he had created. Professional editions were launched in 2007, but the last supported free version 1.5.8 is still available on the company’s site.

What is Unit Ncover?

NCover is a code-coverage tool that allows you to measure how much of your source code is “covered” by unit tests. The idea is that while you’re running your unit tests with NUnit, ncover will be working in the background, keeping track of every line of code that’s being executed. After the test is finished, nCover will show a visual report showing which parts of your source code are and are not covered by existing unit tests. There is a tool to help you determine if you have any functionality that is not evaluated by unit tests. While it’s not perfect, it gives a good visual representation if you’re missing some possible tests.

Features of Ncover are:

• Data-Rich Interactive Browser Interface. …
• Pre-Instrumentation. …
• Coverage For Windows Store Apps. …
• Baseline Executions. …
• Condition Coverage. …
• Branch Coverage Visualization. …
• Source Code Syntax Highlighting. …
• Standard Source Code Analytics.

Why use Ncover

NCover Desktop is a major leap forward in the ease and flexibility of code coverage tools. The code coverage collected while testing your .NET code shows NCover the user what code was used during testing and gives a specific measure of unit test coverage. By tracking these statistics over time, you get a solid measure of code quality over the course of the development cycle. The core of the NCover application is a Windows service that starts automatically and runs continuously in the background of your OS (Windows XP and higher). NCover can monitor applications with .NET version 2.0 and higher, and Silverlight 4.0 and higher.

Advantages of NCover

• CENTRALIZED DATA
• INDUSTRY-LEADING CODE COVERAGE ENGINE
• 32 & 64-BIT SUPPORT
• BUNDLED SUPPORT
• DOCUMENTATION
• OPTIMIZED MEMORY CONSUMPTION
• AUTO UPGRADE & UPDATE

Disadvantages of a NCover

• Best NCover Alternative
• Big Joanie – “No Scrubs” …
• pulses. – “ …
• Neck Deep – “Boulevard Of Broken Dreams” …
• State Champs – “Stitches” …
• Eat Your Heart Out – “Shape Of You” …
• Tonight Alive – “Little Lion Man” …
• Those Without – “She Will Be Loved” …
• Anti-Flag – “Should I Stay Or Should I Go”

Best resources to learn NCover

https://www.devopsschool.com/blog/?s=Ncover

https://www.ncover.com/resources

https://www.ncover.com/

Free Video Tutorials NCover

NCover Interview Questions and Answers

1. Is NCover free?
The company NCover, LLC began when the founder, Peter Waldschmidt, decided to commercialize the open-source tool he created. … The commercial versions were launched in 2007, but the last supported free version 1.5. 8 is still available on the company site.

2. How do I run NCover?
To open NCover Explorer, click on the Start Menu, select All Programs, then NCover, and finally NCover Explorer. Now, open up Project Options by pressing Control-Shift-R or by clicking on Project Actions and choosing Project Options. This window is where you will get your tests ready to execute under NCover.

3. How do I use NCover in Visual Studio?
Install and Register the NCover Visual Studio Extension
Install/update NCover Desktop.
Select the available Visual Studio extension plug-in check box(es) to include with the Desktop install.
After the Desktop is installed, open Visual Studio and confirm the NCover menu has been added to the Visual Studio main menu.

4. How does NCover work?
NCover uses the .NET Framework profiling API to monitor an application’s execution. When a method is loaded by the CLR, NCover retrieves the IL and replaces it with instrumented IL code. NCover does not change your original IL code; it simply inserts new code to update a visit counter at each sequence point. After the .NET process has shut down the profiler outputs statistics to the coverage file.

5) What kind of output does NCover produce?
By default, NCover outputs an xml file named coverage.xml. This file is the analysis output of NCover which you can load into NCover Explorer to see a graphical representation, including source code highlighting.

6. Which of the following is used for code coverage?
Code coverage tools are available for many programming languages and as part of many popular QA tools. They are integrated with build tools like Ant, Maven, and Gradle, with CI tools like Jenkins, project management tools like Jira, and a host of other tools that make up the software development toolset.

7. What are coverage criteria in software testing?
A coverage criterion is a rule or collection of rules that impose test requirements on a test set [Ammann, Offutt]. The coverage criterion describes test requirements completely and unambiguously.

8. How do I get code coverage in Visual Studio?
On the Test menu, select Analyze Code Coverage for All Tests. You can also run code coverage from the Test Explorer tool window.

9. What is a coverlet collector?
Coverlet is a cross-platform code coverage library. NET, with support for the line, branch, and method coverage.

10. How does a coverlet work?
A coverlet is a bedcovering with sides that hang down a few inches past the box spring, but don’t touch the floor. A coverlet can be tucked in or left untucked if edged with decorative trim. Luxury coverlets can be layered directly over a flat sheet or over a blanket.

11. What is a coverlet console?
coverlet.console. Coverlet is a cross-platform code coverage framework. NET, with support for the line, branch, and method coverage. It works with. NET Framework on Windows.

12. What is coverlet MSBuild?
Coverlet is a cross-platform code coverage library. NET, with support for the line, branch, and method coverage. There are no supported framework assets in this package.

13. What is the C# coverlet?
Coverlet is an open-source project on GitHub that provides a cross-platform code coverage framework for C#. Coverlet is part of the. NET Foundation. Coverlet collects Cobertura coverage test run data, which is used for report generation.

14. What is coverlet Nuget?
Coverlet is a cross-platform code coverage library. NET, with support for the line, branch, and method coverage. There are no supported framework assets in this package.

15. What is a coverlet vs quilt?
The main difference between a quilt and vs coverlet is that coverlets do not have a middle layer of warmth as quilts do. Coverlets are made from a single piece of fabric. The fabric of quilts is in fact quilted, with three layers, but coverlets are not quilted.

16. Is a coverlet the same as a bedspread?
A bedspread is designed to cover the entire bed, up over the pillows, and down to the floor. Some bedspreads have a special pocket to hold a bedspread filler or top-of-bed insert for a plush look and additional warmth. A coverlet is smaller, designed to cover the top of the bed and to hang just past the boxsprings.

16. Can I use a coverlet as a bedspread?
If you are looking for a non-reversible cover to place over a comforter or bedspread set, a coverlet is thin and lightweight enough to layer with. You can use a coverlet on top of other pieces but keep it stand alone too for a minimal look.

17. What is code coverage analysis?
A code coverage analyzer monitors your code at runtime and records information about which lines of code were executed. NCover shows each sequence point in your application along with the number of times that point was executed. Sequence points are generated by the compiler and stored in the debug information (.pdb) files. A sequence point basically corresponds to a single program statement (often a line of code) in your high-level language.

18. Why would I want to do code coverage analysis?
Unit test suites are often used as a quality tool during the development process to keep the codebase stable as it changes and expands. Tools such as NUnit are often used to run and report on the test suites. However, when implementing unit testing in your build process, you have no way of knowing how much of your code the unit tests are actually testing. This is where code coverage comes in. You can run NUnit within NCover and use the code coverage report to determine which code was not tested by that particular test suite.

19. What makes NCover better than other code coverage tools?
Unlike most code coverage tools, NCover does not modify your source code in any way to generate coverage information. This means that you get coverage based on real, production code, providing more accurate data. For more information about how NCover works, see “Does NCover require a special compilation for my code?” and “How does NCover work?”

20. Which programming languages does NCover support?
In short, NCover supports any .NET programming language. NCover requires the .NET framework version 2.0.50727 to be installed; however, the application being profiled can be written against any shipping version of the framework. NCover has been tested profiling coverage of .NET 3.5, .NET 3.0, .NET 2.0 and .NET 1.1 applications. Note that NCover derives a lot of information from debugging symbols (.PDB files), which are not governed by any standard. This means that different languages and compilers will yield slightly different output.

Hi everyone, today our topic is related to the software development lifecycle. In other words, what is the life cycle followed during software development? Everything we do in our day-to-day life has a lifecycle involved in it knowingly or unknowingly, we are following a lifecycle or a pattern. For example, we make tea or coffee everyday morning to power our day. What is the procedure for making tea boiling water adding milk adding tea powder adding some sugar? These are the following steps that everybody follows during the time of tea making. The method in which the ingredients are added is the life cycle of tea making process. Let’s see. What are the possible life cycles one can follow here some people boil the water first add milk add tea then add sugar that he is ready? For some others, the order is to add milk and water together bring it to boil then add some tea and then sugar. The rest part of them is in boiling water, add tea brew it well then add some milk to the taste, and then add sugar. So, we saw here the lifecycle involves the steps, which is adding a water milk tea powder for sure. The order in which you do? It is based on your preference. If you want to honor a no sugar diet or a diet or a diabetic person adding sugar becomes an optional step in this life cycle. But no matters what life cycle we follow all of us aim for just one thing a refreshing drink to get you started for the day. Similarly, when developing software. There are numerable steps to develop an ideal quality robust software. These ways of things have been identified and proven to work by experienced teams across the globe. So, let’s go through these seven inevitable processes with a software development lifecycle. Step number one is called planning,

Planning: Planning is where the stakeholder’s business team project manager’s program manager’s technical leads everyone sits together and plans on what needs to be built in simple terms the scope of the application and the milestones by which the software should be ready.

Step number two is

Analysis: Analysis where business analysts on the project will analyze the requirements and teachers that are expected out of the software in detail.

Step number three is

Design: This is the stage where the architects and technical team get involved. They participate in the designing process and decide the architecture of the solution.

Step number four is

Implementation: Finally, it is time to start developing the development team gets on it and starts building the software

Step number five is

Testing: Testing the software gets through a testing team cycle where they start finding defects or bugs. These all defects and bugs get assigned back to the development team, they fix and give them back for retest or testing. This cycle continues until the software is ready for deployment.

Step number six is

Acceptance: Once good quality software is ready. The software is ready to stakeholders for their approval if they are happy with it that means it’ll go signal the software can be deployed for being used by the end customers.

Step number seven and the last one after the deployment scene is called the maintenance base part

Maintenance: This is the part where the deployment team and the operations team. Always make sure that the software is running without bugs and always have been running in the environment.

So, these are the seven steps that we saw involved in the software development lifecycle.

Related video:

After listening to all the series of Splunk workflow. From software building to deployment methods, these all software stuff manages by a developer. Without any typical thoughts, I will give you a simple ideology about Splunk’s developer. What do you think about the concept of Splunk developer? From my eyesight views, it is a professional about to perform certain functions like search and reports, charts and dashboards, alerts, or apps in fronts of the concept of Splunk development. With this brief package of software, it makes you expert and advances for the daily updates configure with this software tool. This helps you to create apps, parse the data application, and manages all the programming functions and the rest of the end-point.  

What are the benefits of Splunk Developer?

Without any query. From the above explanation. You get some brief schematic practices performs by the developer and also get some benefits to utilizing it as follows:

• The better understanding of your environment to drive.
• Decisions
• Search and visualize analyzing data (question focused data set)
• Automated reporting.

Why we should learn Splunk Developer?

This question gives you a well-known perception of why should learn the Splunk developer course? Generate more awareness regarding Splunk use-cases, performances, software monitoring, malfunctioning, application functionality, and the software deployment process. These packages are handled by the developer across the whole organization. Through the main context of the developer, it manages and visualizes the application workflow. Due to this reason, you can accommodate this course very precisely in your IT development process.

How our Splunk Developer Course/training would help?

Through this training course, it will teach you how to use the Splunk tool? And the systems function over Splunk. It is totally based on the concept of planning and visualization with the programming language python. If you learn then you may command over any user application data as statistics report, configures the data, modify the app programming, and keeps the user data secrets either can show it publicly, seeing events with delete or save option, and enhances the app programs through a visual editor. After completing this, you may get certification and this will show proof of the result. This will be much better stuff to get attached to this tutorial in future SaaS advances.

Related video:

Under this section. We are going to talk about Splunk power user in which is derived from the word spelunking and now, it works on some methods such as log monitoring, data analyzing, data visualization, machine-generated logs, website, mobile devices, sensors, security devices, and many more. Few methods are describing as log monitoring whenever you are going to work with software developers team, monitoring team, or anything then the role of Splunk will come in data analyzing, visualizing, websites, mobile applications, sensors, and security devices. When sometimes hackers make efforts to hack websites and security devices then Splunk will come to analyze and improve it for better functioning.  

What are the benefits of Splunk power users?

After understanding the formula of Splunk as a power user. As a power user, it manages and authenticates the functions of software development. Now we are going to estimate the benefits of Splunk core power users as follows:-

• Validates skills and knowledge
• Enhances Employability
• Academic Records Shine
• Higher Salaries
• Digital Badging

Why we should learn Splunk power user?

All you would hear about the process in regard to Splunk power users. Now this time, the question raises that why Splunk power users and learning for what use. Nowadays, data is one of the entities that drive the world today. Actually, the data is oil and you know the worth of oil. All you have is lots of data and how you are using it. Now the condition is that access to the data makes it usable and valuable for the users. The spunk enterprises and cloud makes it highly scalable, speed processing, and high productivity, superior security, profitability, and competitiveness. It shows the signification of the stamp of excellence, a substantial increase in pay, helps you get hired, and clear corporate requirements.

How our Splunk power user course/training would help?

  According to the faster-growing world. It is one of the fastest improving technology. Now, you can learn Splunk much faster than any other technology and this training helps you do just that. Splunk is one of the best automation advances that is finding increased applications for big data and in a lot of industrial entities. We are offering the top Splunk power user course that has been created with massive inputs from the industry specialist so that you can learn Splunk as a power user and apply it for real-world phenomena.

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Researchers Len Bass, Dr. Ingo Weber, and Dr. Liming Zhu have offered a widely cited academic definition: DevOps is “a set of practices intended to reduce the time between committing a change to a system and the change being placed into normal production while ensuring high quality.”

The Problem DevOps Solves

To understand what DevOps changes, it helps to understand the friction it replaces. In a traditional software organisation, developers and operations engineers work in separate silos with different priorities, tools, and mental models of the system.

The Developer’s Experience Without DevOps

Developers write code in a local or shared development environment. Once a batch of changes is ready, it gets handed off to operations for deployment — a process that might happen only once or twice a month. The developer moves on to the next feature while that batch sits in a queue.

When the code finally reaches production, it can behave differently than it did in development. Subtle environment differences — configuration, library versions, network rules — cause errors. The developer must now context-switch back to code they wrote weeks ago to diagnose and fix a problem they can barely remember writing.

The Operations Team’s Experience Without DevOps

The operations team owns the production environment. They manage servers, maintain uptime, and are ultimately accountable when something breaks in front of customers. Periodically, they receive a large bundle of new code from developers and must integrate it into a live system with as little disruption as possible.

Because deployments are infrequent and each one is large, they carry significant risk. Operations engineers often spend considerable time coaxing new code to behave correctly in an environment they maintain but did not design the code for. If errors follow a deployment, it is the operations team that must respond — even when root cause lies in code they did not write.

This structural tension — developers optimising for speed, operations optimising for stability — is the core problem DevOps addresses.

How a DevOps Team Works Differently

DevOps removes the handoff wall. Developers and operations engineers collaborate throughout the entire development lifecycle: design, coding, testing, deployment, and post-release monitoring. The key shifts are:

• Smaller, more frequent releases. Instead of batching weeks of work into a single deployment, teams ship small increments of code continuously. Smaller changes are easier to test, easier to debug, and easier to roll back.
• Automation first. Manual steps — building, testing, provisioning infrastructure, deploying code — are automated wherever possible. This reduces human error and makes the pipeline repeatable and auditable.
• Infrastructure as Code (IaC). Infrastructure is defined in version-controlled configuration files rather than configured manually. Tools like Terraform, Ansible, and Puppet make environments reproducible, closing the gap between development and production.
• Continuous monitoring. Application performance, error rates, and user experience are measured in near real-time. Both developers and operations engineers are responsible for acting on that data.
• Shared ownership. The team shares responsibility for the full lifecycle of a service, reducing the blame culture that arises from siloed work.

Core DevOps Principles

Several principles underpin the DevOps way of working:

• Continuous Integration (CI): Developers merge code changes into a shared repository frequently. Each merge triggers an automated build and test run, surfacing integration problems early.
• Continuous Delivery (CD): Every change that passes automated tests is automatically prepared for release to production. The decision to deploy can be made at any time.
• Continuous Deployment: An extension of CD where every passing change is automatically deployed to production without manual approval gates.
• Feedback loops: Short, fast feedback loops between production behaviour and developer action are central. Monitoring and alerting tools provide this signal.
• Blameless culture: Post-incident reviews focus on systemic improvements rather than individual blame, encouraging transparency and learning.

A Brief History of DevOps

DevOps did not emerge from a single moment. It evolved from the Agile System Administration and Enterprise Systems Management (ESM) movements that gained momentum in the mid-2000s, as practitioners began questioning why Agile principles applied inside development teams were not extending to operations.

Key milestones include:

• 2008 — Velocity Conference: O’Reilly’s Velocity conference, focused on web performance and operations, became a gathering point for engineers interested in developer-operations collaboration. Talks at the 2008 and 2009 editions helped crystallise the ideas that would become DevOps.
• 2009 — DevOpsDays, Ghent: Agile practitioner and project manager Patrick Debois organised the first DevOpsDays conference in Belgium, giving the movement its name and a dedicated community.
• 2012 — First State of DevOps Report: Alanna Brown at Puppet launched what became an annual industry survey tracking DevOps adoption, practices, and outcomes. Subsequent editions, produced in partnership with DORA (DevOps Research and Assessment), provided data on the business impact of DevOps practices.

Essential DevOps Toolchains

DevOps practices depend on integrated toolchains — sets of tools that automate and connect different stages of the delivery pipeline. The major categories are:

What DevOps Is Not

DevOps is frequently misunderstood. It is not a job title, a specific product, or a team you stand up and declare done. Hiring a “DevOps engineer” or deploying a CI/CD tool does not make an organisation DevOps. The tools support the practices, and the practices require a genuine cultural shift: shared ownership, trust between teams, tolerance for experimentation, and a commitment to learning from failure.

Why DevOps Matters

Organisations that successfully adopt DevOps practices can deploy code more frequently, recover from failures faster, and maintain higher levels of service stability. The underlying reason is structural: by removing handoff delays, automating repetitive work, and closing feedback loops between production behaviour and engineering decisions, teams spend more time on valuable work and less time on rework, incident response, and coordination overhead.

DevOps is not a destination. It is an ongoing discipline — one that rewards teams willing to continuously examine their own processes and improve them.

From Agile to DevOps: A Natural Evolution

Agile development is an umbrella term covering a range of iterative software development practices — including Scrum, Kanban, SAFe (Scaled Agile Framework), Extreme Programming (XP), and Lean Development. Despite their differences, these methodologies share a common foundation: continuous feedback loops, incremental delivery, and close collaboration between cross-functional teams.

DevOps grew directly out of the agile movement. As agile practices increased software velocity and the frequency of releases, operations teams struggled to keep pace with development. DevOps emerged as a cultural and technical response to that friction — bridging the historically siloed worlds of software development and IT operations.

The term DevOps is widely attributed to Patrick Debois and Andrew Clay Shafer, who began promoting the concept around 2009. It combines "development" and "operations" and signals a shift toward shared ownership of the software delivery lifecycle.

What DevOps Actually Means in Practice

DevOps is not a single tool or product — it is a combination of cultural principles, practices, and enabling technologies. The core goals are:

• Continuous collaboration between development, operations, and business stakeholders
• Continuous delivery of high-quality software at speed
• Automation of repetitive tasks across the build, test, and deployment pipeline
• Fast feedback to catch and fix problems earlier in the development cycle

Tools are enablers of these goals, not the goals themselves. Teams that treat DevOps as a tooling exercise without addressing culture and process often find limited returns.

Why Automation Is Central to DevOps

Manual quality assurance and deployment processes simply cannot scale to match the release frequency that modern agile and DevOps teams target. This is the fundamental reason automation has become inseparable from DevOps practice.

Automation in a DevOps context typically spans several disciplines:

• Test automation: Automated unit, integration, and end-to-end tests run on every code commit, catching regressions without developer intervention.
• CI/CD pipelines: Continuous Integration and Continuous Delivery pipelines automatically build, test, and deploy code changes, reducing the time from commit to production.
• Infrastructure as Code (IaC): Tools like Terraform and Ansible allow infrastructure to be provisioned and managed through version-controlled code, eliminating manual configuration drift.
• Monitoring and alerting: Automated observability tools detect anomalies and trigger alerts or self-healing responses without human oversight.

A typical automated CI/CD workflow looks like this: a developer pushes code to a shared repository; a CI server automatically builds the artifact and runs the test suite; results are logged and compared against previous runs; if tests pass, the pipeline deploys the build to a staging or production environment. If tests fail, the pipeline notifies the team and halts deployment. Developers review only the exceptions — not every build step.

The Business Case for DevOps Automation

Automation delivers measurable value beyond faster releases. Key benefits include:

• Consistency: Automated processes execute the same steps every time, eliminating human error in repetitive tasks.
• Scalability: Automated environments can scale to handle larger workloads without proportional increases in headcount.
• Developer productivity: By offloading mundane tasks to automation, engineers can focus on higher-value work such as feature development and architecture.
• Faster mean time to recovery (MTTR): Automated rollback and self-healing mechanisms reduce the impact of production incidents.

Key DevOps Tool Categories Driving Automation

The DevOps toolchain is broad. The following categories represent the most impactful areas for automation initiatives:

Source Control and Collaboration

Platforms such as GitHub, GitLab, and Bitbucket provide the foundation for collaborative development and serve as the trigger point for automated pipelines. Every merge request or pull request can automatically kick off build and test workflows.

CI/CD Platforms

Tools like Jenkins, GitLab CI, GitHub Actions, and CircleCI orchestrate the automated build, test, and deployment lifecycle. CircleCI, for example, is a cloud-native CI/CD platform that focuses on speed and configurability for teams of varying sizes.

Artifact Management

Artifact repositories such as JFrog Artifactory store and manage build outputs, container images, and dependencies. Integrating artifact management into the pipeline ensures that only validated, versioned artifacts are promoted to production environments.

Security Scanning

Shifting security left — integrating vulnerability scanning earlier in the pipeline — is increasingly a standard DevOps practice. Tools such as JFrog Xray scan artifacts for known vulnerabilities before they reach production, automating a step that would otherwise require manual security review.

Infrastructure Automation

Terraform, Pulumi, Ansible, and similar tools enable teams to define, provision, and manage infrastructure through code. This removes manual server configuration, reduces drift, and makes environment creation repeatable and auditable.

Remote Work and the Acceleration of DevOps Adoption

The shift to distributed and remote work environments that accelerated in 2020 placed additional pressure on IT teams to deliver digital services reliably and at scale. When teams are geographically dispersed, automated pipelines become even more important — they replace the informal coordination that happens naturally when developers and operations staff share a physical space.

Cloud-based DevOps platforms are particularly well-suited to distributed teams because they are accessible from anywhere, require no on-premises infrastructure, and scale elastically with demand. The period of widespread remote work served as a forcing function for many organizations to modernize their delivery pipelines and invest in DevOps tooling they had previously deferred.

Getting Started with DevOps Automation

Organizations beginning or maturing their DevOps automation journey should consider the following practical steps:

1. Audit your current pipeline — Identify manual steps that are repetitive, error-prone, or slow. These are the highest-value automation targets.
2. Start with CI — Implementing automated builds and tests on every commit provides an immediate feedback loop and a foundation for further automation.
3. Standardize environments — Use containers or IaC to ensure that development, staging, and production environments are consistent.
4. Integrate security early — Automate dependency and vulnerability scanning within the pipeline rather than as a gate at the end.
5. Measure outcomes — Track metrics such as deployment frequency, change failure rate, and MTTR to quantify the impact of automation investments.

Conclusion

DevOps tools are not simply convenience features — they are the operational infrastructure that enables organizations to deliver software continuously, reliably, and securely. As software systems grow in complexity and release cadences continue to accelerate, the teams that invest in robust automation across their pipelines will maintain a significant competitive advantage. The cultural shift toward shared ownership between development and operations, combined with the right tooling, is what transforms automation from a one-time project into a sustainable, scalable practice.

1. Universal Repository Manager

A universal repository manager gives development teams a single, organized location to store and manage the artifacts that make up a modern software project: container images, language packages (npm, Maven, PyPI, NuGet, etc.), and Helm Charts for Kubernetes deployments. Without a centralized manager, artifacts can become scattered across different registries and storage locations, creating inconsistencies that slow delivery and introduce security gaps.

Key benefits of a universal repository manager include:

• Consistency across environments: Developers, CI pipelines, and production systems all pull from the same source of truth, reducing “works on my machine” failures.
• Access control and audit trails: Fine-grained permissions ensure only authorized pipelines can publish or consume specific artifact types.
• Faster builds: Proxying and caching upstream registries locally reduces external network round-trips, cutting build and pull times.
• Scalability: Enterprise-grade managers are designed to handle high-concurrency artifact requests as teams and user bases grow.

Because releases become more predictable and controlled, the downstream effect on end-users is fewer regressions, faster update cycles, and more stable software in production.

2. DevOps Toolchains

DevOps is as much a cultural shift as it is a technology choice, but the right toolchain is what turns DevOps principles into measurable outcomes for end-users. A well-integrated DevOps toolchain automates the repetitive work between a code commit and a production deployment, covering continuous integration (CI), continuous delivery (CD), security scanning, and infrastructure-as-code.

How DevOps tooling improves end-user experience:

• Faster release cycles: Automation removes manual handoffs, compressing the time between a feature being ready and users benefiting from it.
• Shift-left security: Scanning dependencies and container images early in the pipeline prevents vulnerable builds from reaching end-users.
• Reliable rollbacks: Versioned artifacts combined with automated deployment pipelines make rolling back a bad release a predictable, low-risk operation.
• End-to-end visibility: Dashboards that trace a change from commit through build, test, and deploy help teams identify bottlenecks before they affect users.

Popular open-source and commercial options exist across every stage of the pipeline. The best choice depends on your team size, existing infrastructure, and the languages and platforms you target.

3. Personalization and Behavioral Analytics Tools

Modern users expect software that adapts to their context and preferences. Personalization resources allow applications to tailor content, workflows, and recommendations based on user behavior, demographics, or explicit settings. Even straightforward personalization—greeting a returning user by name, remembering their last screen, or surfacing relevant content first—measurably improves engagement and retention.

At a more sophisticated level, behavioral analytics platforms capture interaction data—click paths, session duration, feature adoption rates—and surface patterns that product and engineering teams can act on. Practical applications include:

• Identifying which features drive retention versus which ones users abandon.
• Running A/B tests to validate UX decisions with real usage data rather than assumptions.
• Segmenting users by behavior to prioritize development work toward the highest-impact improvements.

When implementing behavioral analytics, teams must balance insight collection with user privacy obligations. Always review applicable data protection regulations (such as GDPR or CCPA) before capturing or storing user behavior data.

4. Prototyping and Interactive Design Tools

Catching usability problems before a single line of production code is written is far cheaper than fixing them after release. Prototyping tools let designers and developers convert static wireframes into clickable, interactive mockups that stakeholders and test users can evaluate as though they were using the finished product.

The value prototyping brings to end-user experience:

• Early validation: Usability issues—confusing navigation, unclear calls-to-action, inaccessible form layouts—surface during low-cost design reviews rather than expensive post-launch patches.
• Rapid iteration: Changes to a prototype are made in minutes, enabling multiple rounds of user feedback in a short timeframe.
• Stakeholder alignment: An interactive prototype communicates intended behavior far more precisely than a written specification, reducing misunderstandings between design, engineering, and product teams.
• Accessibility testing: Prototypes can be evaluated against accessibility guidelines (such as WCAG) before development begins, ensuring inclusive design from the start.

Some advanced prototyping platforms also integrate automated heuristic analysis, flagging common UX anti-patterns and suggesting improvements based on established design principles.

5. Performance Monitoring Systems

Deploying software is not the end of the engineering team’s responsibility—it is the beginning of a continuous improvement cycle. Performance monitoring systems instrument your application and infrastructure to collect real-time metrics on the factors that matter most to end-users: page load times, error rates, API response latency, uptime, and resource utilization.

What effective monitoring enables:

• Real User Monitoring (RUM): Captures performance data from actual user sessions across different devices, browsers, and network conditions—revealing issues that synthetic tests miss.
• Alerting and on-call workflows: Automated alerts notify engineering teams the moment a key metric degrades, reducing mean time to detection (MTTD).
• Root-cause analysis: Distributed tracing connects a slow end-user request back to the specific service, database query, or infrastructure component responsible.
• Trend analysis: Long-term performance dashboards reveal gradual degradation—such as a steadily growing database query time—before it becomes a critical outage.

Monitoring should cover the full stack: frontend (Core Web Vitals, JavaScript errors), backend (API latency, error rates), and infrastructure (CPU, memory, disk I/O, network). Teams that monitor proactively resolve issues before the majority of users notice them, protecting satisfaction and trust.

Putting It All Together

No single tool delivers a great end-user experience on its own. The five resource categories above work best when used together as part of a coherent engineering strategy:

1. Use a universal repository manager to keep artifacts organized, secure, and consistently available throughout the pipeline.
2. Build a DevOps toolchain that automates integration, delivery, and security scanning so releases are fast and reliable.
3. Apply personalization and behavioral analytics to understand your users and continuously adapt the product to their real needs.
4. Invest in prototyping early to validate UX decisions before they are coded into production.
5. Instrument everything with performance monitoring to catch and resolve issues before they reach users.

Teams that treat these resources as ongoing investments—rather than one-time setup tasks—consistently ship software that earns and retains user trust.

Customers to benefit from an integrated DevOps toolchain with unparalleled mainframe operations management and agile application development and delivery

Development Activity Held Steady—But Hours Increased

In the wake of the COVID-19 pandemic, the overall volume of software development activity remained relatively constant, even as developers shifted almost entirely to working from home. A GitHub report from the period showed a brief, regional dip in activity as the pandemic spread, followed by a quick recovery on the public GitHub repository.

More notably, the time each developer spent on the platform increased. Kelly Stirman, Vice President of Product Strategy and Marketing at GitHub, attributed this in part to developers no longer commuting and to extended working days that blended professional and personal tasks. His conclusion was straightforward: “More time to code increases productivity.”

Whether that extra screen time translates directly into higher output is harder to measure. Home environments introduce distractions, caregiving responsibilities, and irregular schedules. The data reflects time on platform, not necessarily lines of production-ready code shipped.

Digital Transformation Projects Became Business Continuity Priorities

Providers of other DevOps platforms reported similar upticks in activity during the same period. Moritz Plassnig, Senior Vice President and General Manager for Software Delivery Management and Software Delivery Automation Cloud at CloudBees, observed that many organizations had already been investing in custom application development to drive digital transformation. The pandemic compressed that timeline dramatically.

“The current situation has only accelerated that trend,” said Plassnig. Projects that were once medium-term strategic initiatives became immediate operational necessities, elevating the importance of reliable, scalable DevOps pipelines.

The Core Challenge: Making Sure the Right Code Gets Built at the Right Time

Distributed teams introduce coordination risk. Fred Simon, Chief Data Scientist at JFrog, identified feedback loops as the central problem: “Everyone needs feedback.”

Without the informal communication that happens naturally in a shared office—a quick question at a desk, overhearing a colleague’s conversation—developers can spend time on the wrong feature, duplicate work, or miss a blocking dependency. To compensate, engineering managers turned to:

• Video conferencing tools such as Zoom for synchronous check-ins and code reviews.
• Cloud-hosted DevOps platforms to ensure every team member has consistent access to CI/CD pipelines, artifact repositories, and shared tooling regardless of location.
• Asynchronous communication embedded directly in DevOps workflows, reducing the need to schedule meetings for every decision.

Asynchronous Collaboration Matters More Than Video Calls

Emrah Samdan, Vice President of Product at Thundra—a platform for securing and observing cloud-native microservices—argued that the communication tooling built into a DevOps platform is more important than ever in a remote-first environment. Developers collaborating asynchronously can share code snippets, review pull requests, and leave contextual comments on their own schedule, which often produces more thoughtful feedback than a rushed video call.

Samdan was direct about the hidden cost of synchronous-first culture: “A lot of productivity is being lost setting up Zoom calls.” The implication for DevOps teams is clear—invest in platforms and workflows that make asynchronous collaboration the default, and reserve synchronous meetings for decisions that genuinely require real-time discussion.

Survey Data: Remote Work Is Becoming Permanent—But Reactions Are Mixed

The organizational question is no longer whether remote work is possible, but whether it becomes the default. A PwC survey of chief financial officers conducted around this time found that nearly half of respondents (49%) planned to make remote work a permanent option for roles that support it.

Developer sentiment, according to a GitLab survey of workers from the same period, was split:

• 52% of employees reported being more productive working remotely.
• 48% reported being more efficient remotely.

The flip side of those numbers is significant: roughly half of the workforce did not share that positive view. Remote work is not universally beneficial, and engineering leaders need to account for varying preferences, home environments, and working styles when designing distributed team structures.

Teams With Remote Experience Had a Head Start

Brendan O’Leary, Senior Developer Evangelist at GitLab, pointed out that the pandemic did not create remote DevOps teams from scratch—it accelerated a trend that was already underway. Many software development teams had been gradually shifting toward more distributed work before COVID-19, building the tooling, documentation, and cultural practices needed to sustain it.

O’Leary noted that resources such as the GitLab Remote Playbook saw a significant increase in downloads during the early months of the pandemic, as teams scrambled to build structure around their new reality.

His broader observation is worth carrying forward: “I think it is a time that will see a real separation of bosses and leaders.” Effective engineering leadership in a remote context goes beyond tracking ticket velocity or deployment frequency. It means creating psychological safety, removing blockers asynchronously, and enabling teams to ship innovative software even without physical proximity.

What DevOps Teams Should Take Away

The experience of 2020 made several things clear for DevOps practitioners operating in distributed environments:

• Cloud-hosted tooling is table stakes. Teams dependent on on-premises infrastructure faced avoidable bottlenecks when offices closed. Migrating CI/CD, artifact management, and monitoring to cloud services reduces location dependency.
• Asynchronous-first workflows reduce coordination overhead. Design your processes so that the majority of decisions can be made and documented without scheduling a meeting.
• Feedback loops must be explicit and fast. In a co-located office, feedback is ambient. In a distributed team, it must be deliberately built into every stage of the pipeline—automated tests, code review processes, and deployment notifications all serve this function.
• Not every developer thrives remotely. Blanket policies in either direction—fully remote or fully in-office—will fail some portion of the team. The best outcomes come from intentional flexibility combined with strong async communication norms.
• Leadership matters more, not less, at a distance. Metrics are necessary but insufficient. Leaders who invest in clarity, autonomy, and documented decision-making will outperform those who rely on physical presence as a substitute for genuine management.

DevOps teams that had already adopted distributed practices before the pandemic demonstrated a measurable advantage. Building those practices into the fabric of how a team works—rather than treating them as an emergency adaptation—is the most durable lesson from this period.

Overview

CloudBees Connect Virtual Summit brought together a global community of developers, CloudBees and Jenkins users, DevOps practitioners, and continuous delivery experts for two full days of focused, immersive learning delivered in a virtual environment. The North American session was scheduled for May 19, 2020 at 11 a.m. EDT, with a separate European session on May 20, 2020 at 11 a.m. CET.

What the Summit Covered

The summit focused on emerging trends and technologies shaping modern software delivery. A central theme was continuous delivery (CD)—the practice of releasing software rapidly and reliably by automating the build, test, and deployment pipeline. Sessions covered how CD reduces deployment risk while maintaining quality, and explored the tooling and cultural shifts required to adopt it at scale.

Key technical topics included:

• CI/CD pipelines with Jenkins and CloudBees Core: Best practices for continuously integrating code and building fully operational delivery pipelines.
• Containers, Kubernetes, and Jenkins X: How to build and manage a cloud-native CD pipeline using these technologies together.
• Automated security and privacy testing: Techniques for embedding continuous security checks directly into Jenkins CI/CD pipelines.
• Feature flags: Using feature flags as a deployment risk-reduction strategy, enabling code to be shipped to production while controlling feature exposure.
• COBOL modernization: How legacy COBOL source code can participate in modern branching, merging, and deployment workflows using GitHub, CloudBees, and Compuware tooling.
• Application testing with Sauce Labs: Building and testing applications using Jenkins Pipelines integrated with Sauce Labs.
• Common Jenkins troubleshooting: Practical solutions for the five most frequently encountered Jenkins problems.

Workshop Format

The summit was structured as a series of hands-on workshops rather than passive presentations. This format was designed to give attendees practical, applicable skills they could implement immediately after the event—covering everything from pipeline construction to security automation to legacy system integration.

Featured Speakers

The workshop lineup included a range of practitioners from both vendors and end-user organizations:

• Oscar Medina — Developer Advocate, CloudBees
• Viktor Farcic — Jenkins X Product Manager, Principal Software Delivery Strategist and Developer Advocate, CloudBees
• Josh Grant — Solution Architect, Sauce Labs
• Robert Kelley — Director of Delivery Engineering, IHG
• Stuart Ashby — DevOps Specialist, Compuware
• Alex Taylor — DevOps Engineer, insightsoftware

Why This Matters for DevOps Teams

Events like CloudBees Connect serve as a practical reference point for teams evaluating their CI/CD maturity. The breadth of topics—from mainframe COBOL pipelines to Kubernetes-native delivery with Jenkins X—reflects the reality that DevOps teams operate across a wide spectrum of technology stacks and organizational maturity levels.

For teams building or improving their delivery pipelines, the key takeaways from the summit’s agenda are relevant beyond the event itself:

• Security should be a first-class concern in any CI/CD pipeline, not an afterthought.
• Feature flags decouple deployment from release, giving teams fine-grained control over what users see and when.
• Kubernetes and Jenkins X together lower the barrier to building scalable, cloud-native delivery workflows.
• Legacy systems (including COBOL) can and should be integrated into modern DevOps workflows using appropriate bridging tooling.

Overview

VMware published its State of Kubernetes 2020 Report in May 2020, based on a survey of 247 individuals actively using Kubernetes inside organizations with 1,000 or more employees. The research was conducted by Dimensional Research and commissioned by VMware. Because the study targeted large organizations—24% of respondents came from companies with more than 2,500 developers—the findings skew toward enterprise-scale deployments rather than small-team or startup usage.

The report covers adoption rates, deployment patterns, perceived benefits, decision-making structures, and the practical challenges teams face when rolling out Kubernetes at scale.

Kubernetes Adoption Is Growing Rapidly

The headline number from the report is a jump in Kubernetes usage from 27% in 2018 to 48% in 2020—nearly doubling in two years. Despite this growth, adoption is still far from uniform across workloads and infrastructure:

• 57% of adopters run fewer than 10 Kubernetes clusters.
• 60% run less than half of their containerized workloads on Kubernetes.
• 59% of respondents are running Kubernetes in production environments.
• Of those in production, only 20% operate more than 50 clusters.

These figures paint a picture of strong but still maturing adoption: many organizations have committed to Kubernetes in production, yet most have not yet migrated the majority of their workloads onto it.

On-Premises Deployments Still Dominate

Despite the widespread narrative around cloud-native infrastructure, the report found that 60% of Kubernetes deployments are on-premises, with vendor-managed cloud deployments accounting for 42%. The report’s authors suggest this reflects enterprise environments that prefer to pilot new technologies within existing, familiar virtual infrastructure before committing to public cloud environments.

This finding is particularly relevant for platform and infrastructure engineers who may be under pressure to move workloads to the cloud. On-premises Kubernetes—using distributions such as VMware Tanzu, Red Hat OpenShift, or upstream Kubernetes on bare metal—remains a primary deployment model in large organizations.

Enterprise Adoption Is Still in Early Stages

The report frames the current moment as early-stage enterprise adoption. At the time of publication, Kubernetes was approximately five years old. The authors note that Kubernetes is expected to displace older container orchestration tools in the coming years, which should drive broader organizational adoption beyond the siloed or experimental use seen today.

For DevOps practitioners, this is a useful reminder: the majority of enterprises are still building the internal expertise, tooling, and processes needed to use Kubernetes effectively at scale. If your organization feels behind, the data suggests you are not alone.

Reported Benefits of Kubernetes Adoption

The report asked respondents what concrete benefits they had seen from adopting Kubernetes. The results were overwhelmingly positive:

• 95% of respondents reported clear benefits from Kubernetes adoption.
• 56% cited improved resource utilization as a top benefit.
• 53% cited shortened software development cycles.
• Additional benefits included containerizing monolithic applications, enabling cloud migration, and reducing public cloud costs.

The breadth of perceived benefits—spanning both developer productivity and infrastructure efficiency—suggests that Kubernetes is delivering value across organizational boundaries, not just within platform teams.

Who Is Making Kubernetes Decisions?

The report provides insight into the organizational dynamics driving Kubernetes adoption:

• Development teams are the primary decision-makers in 38% of cases.
• 83% of respondents said more than one team is involved in selecting a Kubernetes distribution.
• 18% of the time, C-level executives are involved in the decision—a signal that Kubernetes has moved beyond a purely technical conversation into business strategy discussions.

The multi-team nature of these decisions has practical implications. When multiple stakeholders—development, operations, security, and leadership—must align on a Kubernetes distribution, the selection process becomes more complex and politically charged than a straightforward technical evaluation.

Key Challenges and Pain Points

Despite the optimism around Kubernetes, the report is candid about the difficulties organizations face:

• 40% cite lack of internal alignment as a challenge when selecting a Kubernetes distribution.
• 70% identify a knowledge gap in Kubernetes management as a significant challenge.
• 67% cite insufficient knowledge of Kubernetes deployment techniques.
• Other common hurdles include integrating Kubernetes with existing infrastructure and meeting security compliance requirements.

The knowledge gap finding is particularly striking. Even in large enterprises that are actively running Kubernetes in production, the majority of respondents reported that their teams lack sufficient expertise in core operational areas. This points to a persistent skills shortage that training programs, internal documentation, and platform engineering teams need to address.

The Executive–Developer Disconnect

One of the more nuanced findings concerns how executives and developers perceive barriers to productivity:

• 49% of executives identify integration of new and existing technologies as the biggest impediment to developer productivity.
• Only 29% of developers agree—instead, 29% of developers point to access to infrastructure as their top hurdle.

This disconnect matters for anyone trying to make the case for platform investment. When executives and developers diagnose different root causes, it can lead to misaligned tooling investments and organizational friction. Notably, 55% of developers report having self-service access to Kubernetes resources—suggesting that where self-service models are implemented, they are helping to reduce that infrastructure access bottleneck.

Report Scope and Limitations

The sample size of 247 respondents is relatively small for a report making broad claims about industry trends. The report is also acknowledged to skew toward technology and financial services companies, though other sectors including education, manufacturing, and healthcare were represented.

Readers should also bear in mind that the report was commissioned by VMware, a vendor with commercial interests in the Kubernetes ecosystem. The findings are useful directional indicators, but should be considered alongside other independent surveys and research when informing strategic decisions.

Summary: Key Takeaways

• Kubernetes enterprise adoption nearly doubled between 2018 and 2020, from 27% to 48%.
• Most enterprise deployments are still on-premises, not in the public cloud.
• Adoption is real but partial: most organizations run fewer than 10 clusters and have not yet migrated the majority of their workloads.
• 95% of adopters report measurable benefits, with resource utilization and faster development cycles cited most often.
• Knowledge gaps in management and deployment remain the dominant operational challenge.
• Decision-making is a multi-team exercise in most organizations, with developers leading but executives increasingly involved.
• Self-service infrastructure access is correlated with developer productivity gains.

That vision sounds compelling on paper, but it often triggers anxiety in one corner of the organisation: the IT service desk. Service professionals worry about being left behind, losing influence, or becoming irrelevant as DevOps teams take the wheel. Those concerns are understandable — but they are largely unfounded. DevOps and IT Service Management (ITSM) are not rivals. Handled correctly, they are complementary disciplines that reinforce each other.

Why Service Teams Feel Threatened by DevOps

When DevOps initiatives are announced, service desk staff frequently ask the same question: What happens to us? The concern usually comes from a misunderstanding of what DevOps actually changes. DevOps improves how products are built and run; it does not eliminate the need for structured service delivery. Customers still raise incidents, request changes, and need support — regardless of how quickly or elegantly the development pipeline runs.

The real risk is not that DevOps replaces the service function. The real risk is that the two operate in isolation, each moving at its own pace and without awareness of the other’s priorities.

DevOps and ITSM Are Solving the Same Problem

Strip both disciplines back to first principles and you find a shared objective: delivering value to customers. DevOps pursues that goal through faster, more reliable product releases. ITSM pursues it through structured, repeatable service processes that keep customers productive when things go wrong — or when they need help using what DevOps has built.

That common purpose is the foundation for collaboration. Once both teams recognise that they are working toward the same destination, the question shifts from which team is in charge to how do we coordinate effectively.

How DevOps and ITSM Differ in Practice

Understanding the friction points requires acknowledging that the two disciplines operate very differently, even when their goals align.

• Cadence: DevOps teams typically follow Agile methodologies — short sprints, frequent releases, and continuous iteration. ITSM teams are more interrupt-driven. Incidents and service requests arrive unpredictably, so structure and documented processes (often informed by ITIL) are essential to maintaining consistent service quality.
• Scope: DevOps focuses on a defined product backlog — usually a specific set of features or improvements at any given time. The service desk handles the full surface area of everything the organisation has ever shipped, plus everything customers wish it had.
• Feedback loops: DevOps gets feedback through metrics, monitoring, and stakeholder reviews. The service desk gets it immediately and verbally, from customers who are frustrated right now.

Neither approach is wrong. They reflect the different problems each team is solving. The friction emerges when the two operate as separate tracks with no shared visibility.

The Two Pacing Problems That Hurt Both Teams

When DevOps and ITSM are poorly coordinated, two contradictory complaints surface simultaneously — and both are valid.

DevOps moves too fast

A DevOps pipeline that delivers continuous releases is a genuine achievement — but it creates a knowledge gap at the service desk. Agents start receiving calls about features they have not been briefed on. Documentation lags behind the product. Workarounds that worked last week no longer apply. The service desk appears incompetent, not because the team is under-skilled, but because it has been left out of the release loop.

DevOps moves too slowly

From the service desk’s perspective, DevOps can also feel glacially slow. Recurring customer complaints about a broken feature or a missing capability sit in a backlog while the development team works on other priorities. Service agents can only apologise and log tickets; they have no visibility into when a fix will arrive or how to set expectations with customers.

Both problems share the same root cause: a lack of shared information and communication channels between the two teams.

Practical Steps to Align DevOps and ITSM

Solving the coordination problem does not require either team to abandon its working methods. It requires building deliberate communication bridges between them.

1. Keep ITSM informed of the release roadmap

DevOps teams should share upcoming release dates and feature summaries with the service desk ahead of deployments — not after. This gives service agents time to update knowledge base articles, prepare FAQs, and anticipate the questions customers will ask. Even a brief pre-release briefing significantly reduces the knowledge gap.

2. Route service desk intelligence back into the product backlog

The service desk is one of the richest sources of product feedback in any organisation. Recurring incidents, common workarounds, and frequently requested features are signals that the product backlog should reflect. Establish a formal channel — a shared ticketing view, a regular sync meeting, or a tagged ticket category — so that service desk data can inform DevOps prioritisation decisions.

3. Include service representatives in sprint reviews

Inviting a service desk representative to sprint review or demo sessions costs very little. The benefit is significant: service staff understand what is changing before it reaches production, and DevOps teams hear unfiltered customer feedback from the people who talk to customers every day.

4. Align change management processes

ITSM change management processes can feel bureaucratic to DevOps teams accustomed to continuous delivery. Rather than forcing DevOps into a heavyweight change approval board, work together to define a tiered change model — lightweight approvals for standard, low-risk changes; more rigorous review for changes with broad customer impact. This preserves DevOps velocity while maintaining the service governance that ITSM provides.

5. Use shared tooling where possible

When DevOps and ITSM use entirely separate tools with no integration, information silos are almost inevitable. Connecting monitoring and alerting tools to the service desk, or linking incident records to the relevant deployment history, gives both teams shared context and reduces the time spent recreating information that already exists elsewhere.

The Bottom Line

DevOps does not threaten the service function — it makes excellent service harder to deliver without close collaboration. A DevOps team that ships features rapidly but leaves the service desk without briefings, documentation, or a feedback channel will generate customer frustration that the service desk cannot resolve. An ITSM team that insists on heavyweight processes for every change will slow down a DevOps pipeline and create exactly the bottleneck that DevOps was designed to eliminate.

The winning formula is straightforward: treat DevOps and ITSM as two parts of one end-to-end value chain. Break down the silo between product and service the same way DevOps broke down the silo between development and operations. When both teams share information, respect each other’s constraints, and measure success by the same customer outcomes, the result is better for everyone — including the customers both teams exist to serve.

What Is Cloud AI Platform Pipelines?

Google announced the beta of Cloud AI Platform Pipelines, a managed service designed to help data scientists and ML engineers deploy robust, repeatable machine learning pipelines on Google Cloud. The service adds monitoring, auditing, version tracking, and reproducibility to ML workflows that would otherwise be difficult to operationalize at scale.

The core motivation is familiar to anyone who has moved an ML project from experimentation to production. As Ramesh and Unruh wrote in the original blog post:

When you’re just prototyping a machine learning (ML) model in a notebook, it can seem fairly straightforward. But when you need to start paying attention to the other pieces required to make an ML workflow sustainable and scalable, things become more complex.

Cloud AI Platform Pipelines is Google’s answer to that complexity — and its entry point into the broader practice of MLOps.

MLOps: DevOps Principles Applied to Machine Learning

MLOps (Machine Learning Operations) applies DevOps principles — automation, monitoring, version control, and continuous delivery — to the ML lifecycle. A typical ML workflow subject to MLOps practices covers:

• Data preparation and validation
• Feature engineering and analysis
• Model training and evaluation
• Model deployment and serving
• Ongoing monitoring and retraining

Cloud AI Platform Pipelines is designed to be the orchestration layer that ties these stages together in a repeatable, auditable manner.

Architecture: How It Works

Cloud AI Platform Pipelines runs on a Google Kubernetes Engine (GKE) cluster and is accessible through the Cloud AI Platform dashboard in the Google Cloud Console. The service has two major parts:

• Infrastructure layer: Deploys and runs structured AI workflows integrated with GCP services including BigQuery, Dataflow, AI Platform Training and Serving, and Cloud Functions.
• Pipeline tooling layer: Tools for building, debugging, and sharing pipelines and individual pipeline components.

Workflow Orchestration with Argo

Under the hood, the platform uses Argo, an open-source, container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo executes each pipeline on individual isolated pods within the GKE cluster. This architecture allows pipeline components to leverage managed Google Cloud services such as Dataflow, AI Platform Training and Prediction, and BigQuery. Pipelines can also include steps that run large-scale GPU and TPU computation directly in the cluster, taking advantage of GKE features like autoscaling and node auto-provisioning.

Automatic Metadata Tracking with ML Metadata (MLMD)

Every pipeline run benefits from automatic metadata tracking via the ML Metadata (MLMD) library. MLMD logs:

• Artifacts consumed and produced at each pipeline step
• Pipeline parameters used during each run
• Lineage linking input and output artifacts across steps

MLMD is included as part of the TFX SDK but can also be used independently in other contexts.

Choosing an SDK: KFP vs. TFX

Users can define their pipelines using one of two SDKs:

Regardless of which SDK is used, the compilation and submission process is the same: the SDK compiles the pipeline definition and submits it to the Pipelines REST API. The REST API server then stores and schedules the pipeline for execution. Google noted in the original blog post that the two SDK experiences were planned to converge over time.

Key Features for Enterprise ML Workloads

According to the announcement, customers using Cloud AI Platform Pipelines receive:

• Push-button installation directly from the Google Cloud Console
• Pipeline versioning to track changes to pipeline definitions over time
• Automatic metadata tracking of artifacts and executions via MLMD
• Cloud Logging integration and built-in visualization tools
• Seamless integration with BigQuery, Dataflow, AI Platform Training and Serving, Cloud Functions, and other GCP managed services
• Prebuilt pipeline components for common ML workflow steps, plus support for custom components

Portability: The Kubeflow Advantage

Because Cloud AI Platform Pipelines is built on Kubeflow Pipelines — which is open-source and runs on standard Kubernetes — workloads are not strictly locked into Google Cloud. A commenter on a Hacker News thread discussing the announcement noted:

Cloud AI Platform Pipelines appear to use Kubeflow Pipelines on the backend, which is open-source and runs on Kubernetes. The Kubeflow team has invested a lot of time on making it simple to deploy across a variety of public clouds, such as AWS and Azure. If Google were to kill it, you could easily run it on any other hosted Kubernetes service.

This is a meaningful consideration for teams evaluating cloud vendor lock-in risk in their ML infrastructure decisions.

Planned Features at Beta Launch

At the time of the beta announcement, Google outlined the following features on its near-term roadmap:

• Easy cluster upgrades
• Additional templates for authoring ML workflows
• Simplified UI-based setup of off-cluster storage for backend data
• Workload Identity support for transparent, credential-free access to GCP services
• Multi-user isolation — allowing each user to control access to their own pipelines and resources within a shared cluster

Context: Google’s Broader MLaaS Strategy

Cloud AI Platform Pipelines is part of Google’s broader Machine Learning as a Service (MLaaS) portfolio, which includes Cloud AutoML, Kubeflow, and AI Platform Prediction. The service positions Google more directly against competing managed ML platforms such as Amazon SageMaker and Azure Machine Learning.

For DevOps and platform engineering teams tasked with supporting data science organizations, Cloud AI Platform Pipelines represents a significant step toward treating ML workflows with the same operational rigor as software delivery pipelines — with versioning, auditability, and reproducibility built in from the start.

Further Reading

For implementation details, refer to the official Google Cloud AI Platform Pipelines documentation and the Kubeflow Pipelines project on GitHub. The original announcement was covered in depth by InfoQ.

The Collaboration Challenge in Modern SaaS Delivery

In today’s cloud-native and SaaS landscape, software teams are expected to ship updates daily or even multiple times per day. The traditional waterfall hand-off model — where developers write code, QA tests it, and operations deploys it in discrete, sequential stages — cannot keep pace with that cadence. Yet simply accelerating the pipeline without improving cross-team visibility creates its own problems: outages, unstable releases, and mounting operational debt.

Observability-Driven Development (ODD) is one architectural and cultural response to this challenge. Rather than treating observability as something bolted on after deployment, ODD makes it a first-class concern throughout the entire development lifecycle.

What Is Observability?

Observability, in the context of software systems, refers to how well you can understand the internal state of a system by examining its external outputs. The three primary pillars are:

• Logs: Structured or unstructured records of discrete events within the system.
• Metrics: Numeric measurements aggregated over time (error rates, latency, throughput, resource utilization).
• Traces: End-to-end records of a request’s journey through distributed services.

Together, these signals allow engineering and operations teams to ask arbitrary questions about system behavior — including questions that weren’t anticipated when the system was built. This distinguishes observability from simple monitoring, which typically only answers pre-defined questions.

What Is Observability-Driven Development (ODD)?

Observability-Driven Development extends the concept of test-driven development (TDD) into the operational domain. Just as TDD asks developers to write tests before or alongside code, ODD asks developers to instrument their code — with logging, metrics, and traces — as an integral part of writing it, not as an afterthought.

The practical effect is that development teams actively consider:

• What signals will the operations team need to monitor this feature in production?
• What failure modes are likely, and how will they surface in logs or metrics?
• What KPIs define healthy behavior for this component?
• How does this code interact with shared infrastructure (queues, caches, databases) in ways that could degrade other services?

By building this operational mindset into the development phase, teams increase the chances of detecting both predictable and unpredictable failure permutations before they cause significant customer impact.

Why ODD Matters for Cloud and Distributed Architectures

The need for ODD becomes more acute as architectures become more distributed. A monolithic application has relatively few failure points; a microservices system running on Kubernetes, communicating over message brokers such as Kafka or RabbitMQ, and spanning multiple cloud regions has an exponentially larger failure surface. No single team member can hold the entire system’s state in their head.

ODD addresses this by ensuring that rich observability signals are emitted from every service, making the system’s behavior legible to the entire organization — not just to the team that built a particular component.

The Cost of Insufficient Collaboration Between Dev and Ops

When development and operations teams operate in silos — especially across different locations or time zones — several problems compound:

• Poor hand-offs: Operations receives code without context about expected behavior, making incidents harder to diagnose.
• Ad hoc changes: Pressure to fix production issues leads to undocumented changes that destabilize release cycles.
• Unclear ownership: Ambiguity over who is responsible for a given feature or failure leads to delayed responses.
• Delayed delivery: Rework caused by late discovery of operational issues slows the overall release cadence.

ODD directly addresses these pain points by creating a shared vocabulary — metrics, KPIs, and operational standards — that both teams contribute to and rely on.

Benefits of Adopting ODD

Organizationally and technically, ODD can deliver the following benefits when implemented consistently:

• Common goals: Developers and operations teams align around shared definitions of reliability and performance.
• Shared metrics: A single source of truth for application health that every stakeholder can reference.
• Faster issue detection: Richer instrumentation means anomalies surface earlier, reducing mean time to detection (MTTD).
• Performance guidelines: Agreed-upon thresholds (latency budgets, error rate ceilings) that inform both development decisions and incident response.
• Application governance: Visibility into how code changes affect system-wide behavior, supporting compliance and SLA management.
• IT and business alignment: Business KPIs can be tied directly to technical metrics, making the impact of engineering work legible to non-technical stakeholders.
• Improved team satisfaction: Fewer production fires and clearer ownership reduce burnout for both developers and SREs.

How to Integrate ODD into Your R&D Practices

Adopting ODD is as much a cultural shift as a technical one. The following steps provide a practical starting point:

1. Educate Development Teams on Operational Realities

Developers should understand how their code behaves in production — not just in unit tests. This means familiarizing teams with infrastructure-level monitoring (e.g., Kubernetes pod metrics, message queue lag in Kafka or RabbitMQ), application performance metrics (transactions per second, p99 latency, error rates), and user experience indicators.

2. Define Observability Requirements Alongside Feature Requirements

For each user story or feature ticket, include a definition of what observable signals will indicate healthy operation. This could be as simple as specifying which log events should fire on success and failure, or as detailed as defining custom metrics and dashboards that need to exist before the feature ships.

3. Instrument Code as You Write It

Treat instrumentation as a deliverable, not a task deferred to the end of a sprint. Use structured logging libraries, OpenTelemetry SDKs, or platform-native tooling to emit logs, metrics, and traces from the moment a service is first written.

4. Include SREs and Operations in Sprint Planning

Operational stakeholders should participate in sprint ceremonies — at minimum in planning and review — to flag operability concerns early and validate that observability requirements have been met before a feature is considered done.

5. Build an Observability Platform That Serves All Roles

A proactive observability platform should provide real-time system status to developers, DevOps engineers, SREs, and business stakeholders — each through views tailored to their needs. Developers need code-level context; operations needs service health and infrastructure state; business teams need KPI dashboards. Investing in tooling that can serve all of these audiences reduces the silos that ODD is designed to eliminate.

ODD as a Cultural Practice

ODD is ultimately a cultural commitment to treating operational health as a shared engineering responsibility, not a problem handed off to a separate team after the fact. Teams that adopt it tend to find that the initial investment in instrumentation pays back quickly through faster incident resolution, more confident releases, and better alignment between what engineering ships and what the business needs.

In an environment where scrum teams are measured primarily on feature delivery velocity, embedding ODD requires explicit organizational support — updating the definition of done, acknowledging observability work in capacity planning, and measuring teams on availability and MTTD alongside throughput.

Done well, ODD doesn’t slow development down. It shifts failure discovery from production — where it is expensive — to development and staging, where it is cheap.

The collision between speed and security is a tension that is not easily resolved. DevSecOps—the practice of embedding security directly into the software development lifecycle (SDLC)—has emerged as a leading methodology for helping development and security teams work through these conflicting priorities. Despite its growing adoption, however, security still lags behind as a fully integrated part of most application development pipelines.

A report developed by 451 Research and sponsored by Synopsys (published in October 2019) found that most organizations employ far more application developers than information security professionals, and that only 9 percent of security budgets at surveyed organizations were dedicated to application security. This matters because Verizon’s 2019 Data Breach Investigations Report found that over 60 percent of the hacking attempts analyzed targeted web applications.

The DevSecOps Cultural Shift

One of the most persistent obstacles to DevSecOps adoption is cultural, not technical. Developers naturally want to ship new features quickly; security teams are often still perceived as the “Department of No,” slowing releases to patch vulnerabilities before they can be exploited. Bridging this divide requires security teams to move from gatekeeping to enablement.

Michelle McLean, then vice president of product marketing at security firm StackRox, articulated the key insight clearly:

“It’s important to understand that developing DevSecOps means integrating security into DevOps practices, not the other way around. Integrating security into DevOps creates massive impact on the developer culture and what developers see as important.”

Michelle McLean, StackRox

McLean also emphasized that security teams must go into problem situations with a clearly defined issue, offer multiple resolution options rather than mandates, and collaborate with DevOps teams to jointly craft the fix. This cooperative framing is central to making DevSecOps work in practice.

With that cultural foundation in mind, three concrete trends are shaping how teams operationalize DevSecOps.

Trend 1: Better Tooling Across the Full Application Lifecycle

Historically, security checks were bolted on at the end of the development cycle—or worse, only performed reactively after a breach. Modern DevSecOps requires tooling that covers the entire application lifecycle: build, deploy, and run.

McLean highlighted the importance of catching vulnerabilities early:

“In DevSecOps, it’s just as important—arguably more important—to identify security gaps at build and deploy, such as container and Kubernetes vulnerabilities and misconfigurations, as during runtime.”

Michelle McLean, StackRox

Practically, this means moving beyond runtime detection tools and adopting solutions that:

• Automatically flag risky builds and risky deployment configurations.
• Use orchestration context (such as Kubernetes namespace and workload metadata) to prioritize risk.
• Integrate security feedback directly into CI/CD pipelines so developers receive actionable alerts without leaving their existing workflow.

The goal is to make security tooling a natural extension of the developer experience rather than a separate, adversarial process.

Trend 2: Automation to Close the Security Gap at Scale

No operations team has the bandwidth to manually remediate every vulnerability flagged by scanning tools. The volume of alerts generated by modern security tooling can overwhelm even well-staffed teams, leading to alert fatigue and deferred fixes.

Thomas Hatch, then CTO and co-founder of SaltStack, pointed to automation as the practical answer:

“We are finally seeing a trend where developers are ensuring that deployed code and environments are secure on Day 0, rather than being dictated solely by the opportunity to deploy new apps.”

Thomas Hatch, SaltStack

Automated security remediation—where tooling can apply configuration fixes, revoke over-permissive access, or quarantine a vulnerable container without requiring manual intervention—shifts security from a reactive cost center to a proactive quality layer. When automation is wired into CI/CD pipelines, security checks and fixes happen at the same cadence as feature deployments, rather than lagging behind.

Automation also helps security teams make the business case for DevSecOps: demonstrating that security processes do not slow delivery, but rather protect the delivery pipeline itself.

Trend 3: Accounting for an Expanded Attack Surface

Modern cloud-native architectures—microservices, containers, serverless functions, and complex API meshes—dramatically expand the attack surface compared to traditional monolithic applications. Default configurations in many platforms and frameworks are tuned for ease of use, not security hardness, requiring explicit hardening steps before production deployment.

Jack Mannino, CEO of nVisium, a firm that specializes in integrating security into development processes, noted that issues such as server-side request forgery (SSRF)—where an attacker tricks a server into making requests to internal resources—are receiving more attention precisely because distributed architectures create more trust relationships between components that can be abused.

Mannino’s recommendation:

“It is important that we perform security testing, during development and in production, from different angles to ensure we’re exercising as many code paths as possible.”

Jack Mannino, nVisium

Concretely, this means combining multiple testing strategies rather than relying on a single approach:

• Static Application Security Testing (SAST): Analyzes source code for known vulnerability patterns without executing the application.
• Dynamic Application Security Testing (DAST): Tests running applications by simulating external attacks.
• Software Composition Analysis (SCA): Identifies vulnerable open-source dependencies.
• Infrastructure-as-Code (IaC) scanning: Checks Terraform, CloudFormation, Helm charts, and similar configuration files for misconfigurations before deployment.
• Penetration testing: Human-led adversarial testing to find logic flaws that automated tools miss.

Putting It Together: A DevSecOps Mindset

DevSecOps is not a product you buy or a single tool you deploy. It is an organizational practice built on three pillars: a culture where security is a shared responsibility, tooling that makes secure behavior the path of least resistance for developers, and automation that keeps pace with modern release cadences.

The three trends above—better lifecycle tooling, automation-driven remediation, and multi-angle testing for an expanded attack surface—are concrete starting points for engineering teams looking to mature their DevSecOps practices. Progress in any one of these areas reduces risk; progress across all three creates a genuinely resilient delivery pipeline.

Original reporting sourced from insights.dice.com. Quotes attributed to Michelle McLean (StackRox), Thomas Hatch (SaltStack), and Jack Mannino (nVisium).

UBank Open-Sources Its Internal iOS Accessibility Toolkit

Australian digital bank UBank has released an open source accessibility kit on GitHub, aimed at helping iOS app developers and contributors audit and improve the accessibility of their applications. The toolkit targets users who experience challenges such as low vision, cognitive impairment, or neurological impairment.

UBank’s digital banking chief product officer Peter O’Malley described the release as central to the bank’s mission of “making technology accessible to everyone.” Speaking to ZDNet, O’Malley noted a clear gap in the market: “There’s no easy tool available for developers or contributors to make sure their apps are more accessible, and so for us, we want to ensure technology is accessible and easy for people to use no matter who they are.”

From Internal Testing Tool to Public Resource

The kit was originally built as an internal testing tool by UBank’s in-house development team. By open-sourcing it on GitHub, UBank is making it available to the broader iOS development community — including developers, designers, testers, and product owners — who can use it to audit the accessibility of their own apps.

What the Accessibility Kit Does

The toolkit works by rendering a visual overlay directly on top of a running iOS app. This overlay highlights areas where the app meets established accessibility criteria, flags areas needing improvement, and provides actionable guidance on what changes to make.

According to O’Malley: “It takes all of the objectivity of building an app and what makes it accessible by design.”

Key accessibility checks supported by the kit include:

• Colour contrast levels: Verifying that foreground and background colours meet contrast ratio requirements for users with visual impairments.
• Label structure and wording: Checking whether UI element labels are correctly structured and appropriately worded for assistive technologies such as VoiceOver.
• Touch target accessibility: Assessing whether the app’s interactive elements allow seamless interaction without limitations for users with motor impairments.

Why iOS Accessibility Tooling Matters

Accessibility auditing is often treated as an afterthought in mobile development cycles. Many teams rely on manual review against guidelines such as the Web Content Accessibility Guidelines (WCAG) or Apple’s own Human Interface Guidelines, both of which require significant domain knowledge to apply correctly. Automated overlay tools like UBank’s kit reduce the barrier for non-specialists — such as product owners and designers — to participate in accessibility reviews earlier in the development process.

Apple’s native UIAccessibility framework provides APIs that developers can use to expose accessibility metadata, but tooling that aggregates and surfaces violations at runtime in a visual format is comparatively rare in the open source ecosystem, making UBank’s contribution notable for the iOS community.

UBank’s Broader Digital Agenda

UBank, which operates under a banking licence provided by its parent company National Australia Bank (NAB), has been active in pursuing digital-first product development. Alongside the accessibility kit release, O’Malley discussed the bank’s plans to enhance its budgeting tool — developed in partnership with open banking fintech Basiq — describing it as “a cool fitness tracker for your finances.”

Future development of the budgeting feature is intended to aggregate customers’ financial data from other banks they hold accounts with, delivering a unified financial view. O’Malley framed accessibility as integral to this goal: “As we draw on additional data and additional insights from customers of different financial institutions, we hope we can provide one of the easiest and simplest experiences for customers to pull all their information in one place.”

Finding the Kit

The accessibility kit is published on GitHub under UBank’s open source account. Developers interested in iOS accessibility tooling should search GitHub for UBank’s repositories directly, as repository URLs may change over time.

Originally reported by ZDNet Australia. Peter O’Malley’s title was updated on 4 February 2020 to reflect his role as digital banking chief product officer at the time of publication.

High-Profile Incidents That Put DevOps Security in the Spotlight

In late September 2019, Avast disclosed that it had detected an intrusion into its internal network via a temporary VPN profile that lacked two-factor authentication. Security researchers believed the attackers’ goal was to plant malware inside CCleaner—a widely used system-cleanup utility—repeating a technique that had succeeded in 2017, when cybercriminals successfully trojanized CCleaner and distributed it to millions of users. The 2019 attempt was detected and disrupted before any official build was compromised.

Earlier in April 2019, Docker disclosed a breach of a single Docker Hub database that exposed sensitive information from approximately 190,000 accounts. Investigators suspected the attackers may have attempted to implant malware in official Docker images used to instantiate containers. Docker confirmed that no official images were ultimately compromised, but the incident underscored how a single point of failure in a container registry can threaten countless downstream environments.

These are examples of software supply chain attacks—a category in which adversaries compromise widely used software or infrastructure upstream so that the malware propagates automatically to every organization that installs or pulls from that source. The technique is attractive precisely because a single successful intrusion can reach millions of targets simultaneously.

Why DevOps Environments Are Prime Targets

1. Rapid Cloud Adoption Creates Sprawl

DevOps pipelines are increasingly running on public cloud platforms. Keeping pace with the dynamic nature of cloud environments—constantly changing components, configurations, permissions, and services—places an enormous operational burden on teams that are already moving fast. The resulting configuration drift can leave critical gaps, including:

• Accounts without multi-factor authentication (MFA/2FA) enabled
• Object storage buckets misconfigured with public read or write access
• Container API endpoints exposed without access controls
• Serverless function invocation URLs left unprotected
• Over-permissioned IAM roles and service accounts

2. Modern Software Is a Web of Third-Party Components

Software development today rarely means writing every line from scratch. Production applications routinely depend on dozens or hundreds of open-source packages, commercial SDKs, base container images, and CI/CD plugins. Each dependency is a potential entry point. Compromising any single component in this ecosystem—a popular npm package, a PyPI library, a Docker base image—can give an attacker an immediate foothold across every project that consumes it.

What DevOps Teams Can Do to Defend Against These Attacks

Preventive controls such as MFA, firewalls, web application firewalls (WAF), anti-malware tools, and secure email gateways are necessary but no longer sufficient on their own. Sophisticated supply chain attackers routinely bypass these layers. A robust strategy must combine prevention with continuous monitoring and rapid incident response.

Monitor DevOps Pipelines and Cloud Infrastructure

Early detection is the single most important factor in limiting blast radius. Every meaningful activity in the DevOps workflow—code pushes, build triggers, artifact publishes, and deployments—should be logged and profiled against a baseline of normal behavior. Anomalies to investigate include:

• Access from unexpected geographic regions or IP ranges
• Logins outside normal working hours
• Unusual changes to build configurations or pipeline definitions
• Artifact publishes that do not correspond to a recognized commit or build job

Platform-level event streams—for example, Azure DevOps audit logs, GitHub audit logs, or GitLab’s audit events—can feed a SIEM or monitoring platform to provide this visibility without requiring agents on individual build hosts. Underlying cloud infrastructure (virtual machines, storage accounts, management consoles, PaaS services) should be monitored in the same way, since infrastructure-layer compromises are frequently used as a stepping stone to reach the pipeline itself.

Monitor Production Container Environments

Container security requires visibility at multiple layers:

• Intra-container events: Process execution, file-system writes, and outbound network connections from within a running container can reveal compromised processes or malware payloads. Achieving this visibility typically requires embedding lightweight monitoring agents or eBPF-based probes as part of the golden image build.
• Container-to-container communication: Profiling expected network flows between containers—using data from the orchestration layer (Kubernetes network policies, service mesh telemetry)—makes it possible to detect rogue containers establishing unexpected connections.
• Image provenance: Enforce image signing and verify digests at deploy time so that only images from trusted registries and with verified signatures can run in production.

Continuously Assess Cloud Configuration

A point-in-time audit is not enough in a cloud-native environment where configuration changes happen constantly. Automated cloud security posture management (CSPM) tools can continuously benchmark running configurations across virtual machines, storage, databases, management consoles, container clusters, and identity policies. The CIS Benchmarks are a widely adopted, vendor-neutral baseline for this kind of assessment and are available for major cloud providers, operating systems, and Kubernetes.

Scan Every Software Component for Vulnerabilities and Malware

Dependency scanning and software composition analysis (SCA) should be mandatory gates in the CI/CD pipeline, not optional afterthoughts. Effective component security includes:

• Scanning open-source dependencies for known CVEs before they are merged or promoted
• Scanning container images for vulnerable OS packages and application libraries
• Analyzing first-party and third-party code for suspicious patterns or embedded malware
• Generating and maintaining a Software Bill of Materials (SBOM) so you can respond quickly when a new vulnerability is disclosed in a component you use

The Bigger Picture

Cybercrime syndicates and state-sponsored threat actors have recognized that compromising software at its source—inside the DevOps pipeline—delivers a much higher return than attacking individual organizations one by one. A single implant in a widely distributed package or container image can propagate to thousands of organizations before anyone raises an alarm.

Defending against this threat model requires treating security as a first-class concern throughout the software delivery lifecycle: enforcing least-privilege access, enabling MFA everywhere, scanning every artifact, monitoring every pipeline event, and being prepared to respond the moment something anomalous appears. The organizations that invest in this discipline now will be far better positioned to detect and contain the supply chain attacks that are certain to continue.