A Cybersecurity Veteran Meets DevOps

After years working on the operations side of IT and in cybersecurity roles, it is easy to become disheartened. The industry and its customers keep wrestling with the same fundamental problems—not just year after year, but decade after decade. Breach patterns repeat. Remediation cycles recur. Investment grows, yet outcomes stagnate.

DevOps, and particularly DevSecOps, looked like a genuine way forward—a philosophy built on rapid iteration, shared responsibility, and stable self-healing infrastructure. For someone with a background in ops, the appeal is obvious: build something small, validate the idea quickly, learn, and adapt. That is the promise of the Minimum Viable Product (MVP) mindset at the heart of DevOps.

The Tool Trap: IT’s Oldest Problem

Traditional IT has long suffered from a focus on technology over outcomes. Decisions get made around which operating system to standardise on, which vendor relationships justify existing contracts, and which staff skill sets constrain technology choices—or vice versa. Large-scale IT projects are frequently dominated by systems integrators who fit customer requirements into their preferred solutions rather than choosing the best tool for the job.

DevOps was explicitly designed to break this pattern. Its founding principles emphasise culture, collaboration, and process over any specific tool chain. CALMS—Culture, Automation, Lean, Measurement, and Sharing—is the framework many practitioners use to describe what DevOps actually is. Notably, none of those five pillars is a product name.

What the Community Actually Talks About

When a newcomer looks at where DevOps conversations happen—forums, conferences, blog posts, Slack communities—a familiar pattern emerges. The discussion often sounds less like a philosophy and more like a vendor showcase:

• Continuous integration becomes a debate about Jenkins versus GitHub Actions versus CircleCI.
• Continuous delivery becomes a loyalty test between Chef, Puppet, and Ansible.
• Containerisation turns into Docker versus Kubernetes configuration wars rather than a conversation about why and when to containerise at all.
• Version control is discussed almost exclusively as Git mechanics rather than branching strategies and collaboration workflows.
• Project tracking carries an implicit assumption that you use Jira—the “Do you even Jira, bro?” problem.

This mirrors the Vi versus Emacs debates that have occupied developer communities for decades: deeply felt, vigorously argued, and largely orthogonal to the actual work being done.

Why This Matters for Practitioners

The risk is not that these tools are bad—most of them are excellent at what they do. The risk is that tool lock-in and tool tribalism recreate exactly the rigidity that DevOps was meant to dissolve. Consider the symptoms:

• Inflexible pipelines: If swapping one component of your CI/CD pipeline requires a multi-month project, your pipeline has become the new monolith.
• Cultural resistance: If team members resist changing a tool because of personal preference rather than technical merit, the culture problem traditional IT had has simply moved to a new context.
• Assumed tooling: Onboarding documentation that assumes specific tools excludes contributors and narrows the team’s ability to adapt.
• Vendor dependency: Proprietary integrations that tightly couple your workflow to a single vendor recreate the systems-integrator trap in a more modern wrapper.

What DevOps Conversations Should Look Like

A healthy DevOps conversation focuses on outcomes and replaces tool loyalty with tool pragmatism. Some useful reframes:

The goal is always to ask what problem the tool solves before asking which tool to use.

The MVP Mindset Is the Right Starting Point

For someone new to DevOps—especially someone coming from ops or security rather than application development—the MVP mindset is the most transferable concept. It applies equally well to a small personal project and to an enterprise pipeline:

1. Define the smallest thing you can ship that proves whether an idea has merit.
2. Instrument it so you can measure whether it works.
3. Automate the repetitive parts so you can iterate without accumulating manual toil.
4. Choose tools that are replaceable, not tools that you commit to permanently.
5. Revisit your process at least as often as you revisit your technology choices.

This approach scales from a solo developer testing a side project to a large engineering organisation running hundreds of microservices. The principles do not change; only the scope does.

A Note for Those Already Deep in DevOps Culture

If you have spent years inside a mature DevOps organisation, it can be easy to lose sight of how the culture appears from the outside. To someone coming from traditional IT or cybersecurity, a community that argues passionately about tools while claiming to be process-first looks uncomfortably familiar.

That outsider perspective is valuable. It is worth periodically asking:

• If a component of our pipeline became unavailable tomorrow, how long would it take to replace it?
• Are our tool choices driven by evidence, or by the preferences of whoever set up the pipeline originally?
• Do our onboarding processes assume tools, or do they explain why those tools were chosen?
• Are we measuring outcomes—deployment frequency, mean time to recovery, change failure rate—or are we measuring tool adoption?

DevOps has produced genuine improvements in how software is built and operated. Preserving that value means staying honest about when the community drifts from process back toward the tool tribalism it was designed to escape.

The Parallel with Cybersecurity

The parallel with cybersecurity is instructive. The security industry has spent decades acquiring tools—firewalls, SIEMs, endpoint agents, vulnerability scanners—without consistently improving outcomes. The tool purchases continued; the breaches continued alongside them. DevSecOps emerged partly as a response: embed security thinking into the development process rather than bolt on security products at the end.

If DevOps repeats the mistake of prioritising tools over outcomes, it risks the same stagnation. The methodology’s strength is its emphasis on feedback loops, shared ownership, and continuous improvement. Those qualities live in culture and process, not in any particular piece of software.

Key Takeaways

• DevOps is a process philosophy, not a tool set. Tool choices should serve the process, not define it.
• Tool tribalism—loyalty to specific products over outcomes—is the same trap traditional IT fell into, now wearing a modern disguise.
• Healthy DevOps communities discuss culture change, feedback loops, and measurement alongside tool options.
• Interchangeable tools are a sign of a mature pipeline; rigid tool dependencies are a warning sign.
• The MVP mindset—ship small, measure, iterate—is the most portable idea in DevOps, applicable from solo projects to enterprise engineering.
• DevSecOps extends this philosophy to security: outcomes over products, shared responsibility over bolt-on tools.

• DevOps has grown exponentially over the last decade.
• In the coming years, it will be impossible for organizations to ignore DevOps methodologies.
• DevOps culture will soon be adopted by almost all organizations in a few years.

In the modern business world, DevOps is the much-needed answer to eliminate repetitive tasks and poor coordination between the Software development and IT Operations team. DevOps is a systematic approach that eliminates repetition, reduces glitches, exemplary coordination, and more production. Before we go in-depth about DevOps, let’s look into its history.

The inception of DevOps

Back in 2008, Andrew Shafer scheduled a seminar on “Agile Infrastructure” in Toronto. He received a very poor response that he closed the seminar before it even began. But the idea struck Patrick Debois. Patrick was keen to collaborate with Andrew. He was excited to know that Andrew had the same viewpoint as him, who felt there was a disconnect between the development and operations team. He strongly felt that the differences between both the teams could be eliminated, and both teams can work in collaboration. Andrew and Patrick came together to form the group Agile Systems Group. They aimed to bring together developers and operators.

In 2009, Patrick watched a presentation by John Allspaw on “10+ Deploys per Day: Dev and Ops Cooperation at Flickr”. John Allspaw spoke about how developers and operators can collaborate to improve the deployment of software. This presentation inspired Patrick to start his seminars. His conferences attracted forward-thinking minds who were trying to improve the deployment of software. The group of people who came together started the DevOpsDays hashtag on twitter. This hashtag started to trend, and more and more people joined the conversation under the hashtag DevOps. They had to reduce from DevopsDays to DevOps due to character limitations.

In 2010, the DevOpsDays face to face meetings was conducted in different cities around the world, which led to the popularity of DevOps.

By 2018 most organizations embraced DevOps in their business process. The ability to support the modern business that is constantly going through a transformation, more and more organizations started adopting DevOps in their processes.

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DevOps trends in 2019

The collaboration between the Development and Information Technology Operations has led to better communication and also the emergence of new disciplines and tools. As a result, more and more organizations started adopting DevOps. The top five trends of DevOps are:

Automation

It will not be a distant reality that most business functions will be automated. We have already adopted automation in quite a few functions. Automation will change how IT operations operate. It will also lead to the development of DevOps processes. The 6C’s of DevOps are:

• Continuous Business Planning
• Collaborative Development
• Continuous Testing
• Continuous Release and Deployment
• Continuous Monitoring
• Collaborative Customer Feedback and Optimization

The implementation of automation between the stages of 6C’s is essential for the development of DevOps.

AI and ML

Artificial Intelligence and Machine Learning are going to be a big part of DevOps culture. AI and ML can perform tedious tasks, along with processing a huge amount of information and save time for IT staff. They can study patterns and provide solutions to potential problems. This will make work easier for IT staff, and they can concentrate and prioritize time for other important work.

More focus on DevOps Assembly Lines

Most organizations are focusing on automating the process of software development. DevOps Assembly Lines focuses on streamlining several DevOps activities, workflows that are event-driven, easy to share statuses as well as information across various activities. DevOps Assembly Line can define complex workflows through numerous pipelines. Earlier the focus was on Continuous Integration (CI), which has been shifted to Continuous Delivery. DevOps Assembly Line can manage several pipelines and automate workflows through several teams and tools, which enables Continuous Delivery.

Container Technology – Kubernetes

Kubernetes has become enormously popular because of its easy to use.  A product originally developed by Google is an open-source platform. It automates the operations of Linux containers. It avoids the manual work involved in deploying and scaling applications that are containerized.

DevSecOps will be in focus

DevSecOps aims at bringing security in every step of the process. Each person is held responsible in the SDLC (Software Development Life Cycle) flow. The security processes and controls are embedded in the early stage of the workflow. This ensures that it is not bolted towards the end of the processes flow.

DevOps is not just a technology but a mindset and culture that more and more organizations are embracing to eliminate disconnections between development and operations. Businesses that are adopting DevOps should keep a tab on the latest trends and developments of DevOps to benefit from it.

In the near future, it is expected that almost all organizations will be adopting DevOps in their functions. This would lead to a high demand for DevOps certified professionals in various organizations. It is highly recommended for developers and operators to get certified in DevOps. So, if you’re an expert and looking for a certification, Certification Planner conducts DevOps courses in the US, Australia, Mexico, Canada, and Singapore. The courses facilitated by the Certification Planner are:

• DevOps Foundation
• DevOps Leader

Every other aspect of application delivery has been scrutinized and optimized for DevOps. Why not re-examine quality metrics as well?

Are classic metrics like number of automated tests, test case coverage and pass/fail rate important in the context of DevOps, where the goal is immediate insight into whether a given release candidate has an acceptable level of risk? What other metrics can help us ensure the steady stream of updates are truly fit for production?

To provide the DevOps community an objective perspective on what quality metrics are most important for DevOps success, Tricentis commissioned Forrester to research the topic. The results are published in the 55-page report, Forrester Research on DevOps Quality Metrics that Matter: 75 Common Metrics—Ranked by Industry Experts. The report takes a deep dive into the global findings, complete with heat maps, quadrant mappings and some fun lists such as “Most Overrated,” “Hidden Gems” and “Top DevOps Differentiators.”

One of the most common questions we received after publishing the report was: “How do the results vary across regions?” In response, we performed some additional regional analysis—and I’d like to share those results here.

To start, let’s take a look at the global top 20. The following metrics were ranked as the most valuable by the DevOps experts who measure them (across all regions).

Europe DevOps Quality Metrics Trends

Looking specifically at Europe, the top 20 changes as follows:

Interesting trends in this region:

• There is a greater commitment to measuring quality metrics. European respondents reported a higher level of DevOps quality metrics measurement across the board. For almost all metrics, the usage rate was at least 6% higher than the global average. For metrics related to time, coverage, risk, effectiveness and efficiency, the usage rate was over 14% higher. This speaks to European organizations’ commitment to scrutinizing and continuously optimizing their quality processes—especially in terms of time and resource utilization.
• Risk and coverage metrics are valued more than in the global average. European respondents also ranked risk and coverage quality metrics a surprising 21% higher than the global average. This could be related to the fact that the respondents from this region came primarily from the financial services and insurance sector, with healthcare and government close behind. In such highly-regulated industries, measuring and mitigating risk is certainly a core concern. This finding could also indicate European organizations place a greater emphasis on protecting the corporate brand.
• Test data preparation time seems to be a greater concern. European respondents were more likely to measure (+18%) and highly-value (+23) time spent preparing test data than their global peers. Given the restrictions GDPR placed on test data as of May 2018, it seems likely that European organizations have significantly changed their test data management processes (e.g., to masking and more synthetic test data generation), and are cautiously monitoring how the changes are impacting their overall efficiency.

Asia Pacific DevOps Quality Metrics Trends

Now, let’s shift focus to Asia Pacific. The following 20 metrics were ranked as the most valuable by the Asia Pacific DevOps experts who measure them:

Notable trends in this region:

• End-to-end testing metrics are valued—and measured—more than in the global average. Although Asia Pacific respondents measured fewer build and functional validation metrics than the global average, they measured (and valued) end-to-end testing metrics much more than their peers around the world. For example, percent of automated end-to-end tests was measured by 47% of the organizations (versus 36% globally) and highly-valued by 84% (versus 70% globally). Risk coverage measurement was significantly higher; it was measured by 49% (versus 34%) and highly-valued by 71% (versus 59%). This speaks to the region’s focus on digital transformation and commitment to delivering exceptional user experiences.
• API testing metrics were also valued—and measured—more than in the global average. Asia Pacific respondents also measured and valued API testing quality metrics more than the global average. Overall, API quality metrics were measured by 16% more organizations in this region than globally. The highest valued API quality metrics were API test coverage (63% versus 39% globally) and API risk coverage (79% versus 62% globally). This prioritization of API testing is likely a side effect of the regional trend towards API-driven open banking (the majority of respondents indicated they were in the financial services and insurance sector).
• There is a greater leader/laggard quality metrics measurement gap. Part of the study involved classifying the respondents as either DevOps leaders or DevOps laggards, based on their responses to various questions about the maturity of their processes. Although the percentage of DevOps leaders in the region was lower than the global average (18% versus 26%), the DevOps leaders from Asia Pacific generally measured quality metrics at a comparable rate to their global peers. However, the DevOps laggards in Asia Pacific generally measured quality metrics at a much lower rate than their global peers. This suggests the select set of firms that have truly prioritized DevOps initiatives have made great strides—and the laggards have a lot of catching up to do in order to remain competitive.

Security as an Unrealised Ideal

The report describes security in DevOps as an “unrealised ideal” — widely discussed, rarely achieved end-to-end. The authors acknowledge the uncomfortable truth: security is not a competitive differentiator in the way that shipping features quickly is. That tension helps explain why so many organisations deprioritise it even when they know better.

Yet the data shows that the organisations willing to do the hard work of embedding security across every phase of the software delivery lifecycle consistently outperform those that don’t — not just on security metrics, but on core delivery metrics as well.

What the Numbers Show

The survey segmented respondents by level of security integration, from no integration at all to security present across all phases of the delivery cycle. The performance gap between these groups is significant:

• DevOps maturity: 21% of respondents with the highest level of security integration reported reaching an advanced stage of DevOps maturity. Only 6% of those with no security integration could say the same.
• On-demand deployment: 61% of high-security-integration firms said they could deploy to production on demand, compared with 49% of firms with no security integration.
• Stopping insecure releases: Security-conscious firms were more than twice as likely to be able to halt a push to production when a moderate security vulnerability was detected, giving their customers meaningful protection against shipping insecure code.
• Security posture improvement: 82% of high-integration respondents said their security practices had significantly improved their overall security posture. Among those with no integration, that figure dropped to 38%.

These figures make a compelling case that investing in security integration is not purely a risk-mitigation exercise — it correlates with better delivery performance across the board.

Why Integration Is So Hard

The report is candid about the difficulty of the journey. Security integration exposes years of accumulated technical debt — the “duct tape and glue” holding legacy systems together. As one layer of complexity is addressed, another emerges. The report describes this bluntly: resolving one roadblock reveals the next, and the process can feel relentless before it becomes routine.

This is consistent with findings from the 2018 edition, which stressed that reaching a state where Dev and Ops work in genuine harmony requires slow, deliberate evolution. In that survey, only about one in ten organisations sat at either extreme of the maturity spectrum; the vast majority — roughly 79% — were somewhere in the middle, still working through the process.

What Advanced Teams Are Doing Differently

The report identifies a clear pattern in what distinguishes the most mature teams from the rest. Organisations at the advanced end of DevOps implementation are:

• Automating security policy configuration — removing manual, error-prone enforcement steps and encoding security requirements directly into the pipeline.
• Exploring automated incident response — moving beyond detection toward automated remediation for known threat patterns.
• Building high-trust, autonomous team structures — enabling application, operations, and security teams to collaborate cross-functionally without creating bottlenecks.
• Treating security as a shared responsibility — rather than delegating it entirely to a security team, delivery teams are empowered and expected to make security improvements themselves.

The combination of automation, organisational trust, and cross-functional ownership is what separates teams that have genuinely integrated security from those that have merely added a security sign-off step at the end of a sprint.

DevOps Principles Apply Directly to Security

Alanna Brown, then senior director of developer relations at Puppet and lead author of the report, summarised the core message clearly:

“The DevOps principles that drive positive outcomes for software development — culture, automation, measurement and sharing — are the same principles that drive positive security outcomes. Organisations that are serious about improving their security practices and posture should start by adopting DevOps practices.”

Alanna Brown, Senior Director of Developer Relations, Puppet

This framing is useful for engineering leaders who struggle to make the case for DevSecOps investment internally. The argument is not simply that security is important — it’s that the practices your team is already using for delivery excellence are the same ones that produce security excellence. The incremental cost of extending them to security is lower than building a parallel security process from scratch.

Key Takeaways for DevOps Teams

• Start with visibility. You can’t automate what you haven’t defined. Map where security checks currently happen in your pipeline and identify the gaps.
• Automate policy enforcement early. Static analysis, dependency scanning, and secrets detection should run on every commit — not as a final gate before production.
• Break down team silos. Security teams embedded in delivery cycles, rather than positioned as an external audit function, correlate strongly with better outcomes in the survey data.
• Measure security outcomes as delivery metrics. Mean time to remediate vulnerabilities, percentage of builds blocked by security failures, and deployment frequency post-incident are all trackable.
• Accept the long arc. The report is clear that full integration takes time. The 2018 data showed most organisations are mid-journey. Progress is incremental, but the compounding benefits are real.

The 2019 Puppet State of DevOps Report does not present a shortcut to DevSecOps maturity. What it does provide is a data-backed argument that the journey is worth taking — and that the same cultural and technical foundations you’ve already laid for continuous delivery are the right starting point for getting security right.

One of the most effective ways modern teams cut that cycle time is by adopting continuous development practices. This article explains what continuous development is, breaks down its three core components, and offers practical guidance on getting started.

What Is Continuous Development?

“Continuous development” is an umbrella term closely associated with Agile and DevOps methodologies. It describes an iterative approach to building and releasing software in which work is integrated, tested, and shipped in small, frequent increments rather than in large, infrequent releases.

The term encompasses three related but distinct practices: continuous integration (CI), continuous delivery (CD), and continuous deployment (CD). Understanding the difference between them is essential before choosing tooling or designing a pipeline.

The Three Pillars of Continuous Development

1. Continuous Integration (CI)

Continuous integration is the practice of merging developer changes into a shared main branch frequently — ideally multiple times per day. Each merge triggers an automated build and a suite of automated tests.

The core goal is to catch integration problems early. When every developer merges small changes regularly, conflicts and regressions surface quickly and in a limited scope, making them far cheaper to fix than defects discovered weeks later. CI does not automatically release anything to customers; it simply validates that the codebase stays in a releasable state.

• Key inputs: source code commits, feature branches
• Key outputs: build artifacts, test reports, pass/fail status
• Common tools: Jenkins, GitHub Actions, GitLab CI, CircleCI

2. Continuous Delivery (CD)

Continuous delivery extends CI by automating the steps required to prepare a release — packaging, environment configuration, integration testing, and staging deployment — so that the software is always in a state that can be released to production. The actual push to production, however, still requires a deliberate human action (a button click, a merge to a release branch, or an explicit approval).

Because changes are released in small batches, issues are easier to isolate and roll back. Teams can release on a predictable, low-risk schedule rather than treating each release as a major event.

• Key inputs: validated build artifacts from CI
• Key outputs: a tested, staged release ready for one-click production promotion
• Distinguishing trait: a human approves the final production push

3. Continuous Deployment

Continuous deployment takes the automation one step further: every change that passes all automated pipeline stages is deployed to production automatically, with no human intervention required. A change is held back only if a test or quality gate fails.

This eliminates the concept of a fixed “release day.” Teams can ship multiple times per day, gather real user feedback almost immediately, and iterate rapidly. The trade-off is that your automated test coverage must be comprehensive enough to act as the sole quality gate — a significant investment.

• Key inputs: validated build artifacts from CI
• Key outputs: live production releases, triggered automatically
• Distinguishing trait: no human approval step before production

CI vs. CD at a Glance

Why Continuous Development Matters for Small Teams

The efficiency gains from continuous development are proportionally larger for smaller teams. When a two- or three-person team replaces manual integration and deployment steps with an automated pipeline, they recover hours each week that would otherwise be spent on error-prone, repetitive tasks. That time compounds quickly into faster feature delivery, faster bug fixes, and a better product.

Beyond speed, continuous development practices reduce risk. Smaller, more frequent releases mean that if something goes wrong, the blast radius is limited and the change causing the problem is easy to identify.

How to Get Started with Continuous Development

If your team is new to CI/CD, start with these foundational steps:

1. Start with CI before CD. Get every developer merging to a shared branch at least once a day, and make sure an automated build and a basic test suite run on every commit. This alone will surface integration problems far earlier.
2. Write tests before adding automation. A pipeline is only as reliable as its tests. Invest in unit tests, integration tests, and at least smoke-level end-to-end tests before automating deployments.
3. Automate your build and packaging steps. Eliminate manual steps in creating deployable artifacts. Reproducible builds are a prerequisite for reliable CD.
4. Add a staging environment. Deploy automatically to a staging or pre-production environment first. This gives you a production-like target for integration tests without touching live users.
5. Choose continuous delivery before continuous deployment. Most teams benefit from the manual approval gate initially. Shift to fully automated deployment only once your test coverage and monitoring are mature.
6. Use containerization to reduce environment drift. Tools like Docker make it easier to ensure that what passes in the CI environment is exactly what gets deployed to production, eliminating the classic “works on my machine” problem.

Choosing a CI/CD Tool

The market for CI/CD tooling is broad. When evaluating options, consider the following factors:

• Integration with your version control system — native support for GitHub, GitLab, or Bitbucket simplifies setup.
• Pipeline-as-code support — storing pipeline configuration in version control (e.g., a Jenkinsfile or .github/workflows YAML) keeps your process auditable and reproducible.
• Container and orchestration support — if you use Docker or Kubernetes, confirm the tool integrates cleanly with those workflows.
• Scalability and pricing — open-source self-hosted tools (Jenkins, GitLab CI) offer flexibility; hosted SaaS tools (GitHub Actions, CircleCI, Buddy) reduce operational overhead.
• Notification and observability hooks — you need to know immediately when a build or deployment fails.

Continuous Development in Context: Agile and DevOps

Continuous development did not emerge in isolation. It builds directly on the principles of Agile software development, which were codified in the Agile Manifesto in 2001, and on the DevOps movement, which extended Agile principles into operations and delivery.

Where Agile introduced iterative development and frequent feedback loops at the team level, DevOps and continuous development extended those loops all the way to production. The result is that organizations have progressively moved from doing basic CI, to adding continuous delivery, to fully embracing continuous deployment — each step reducing the gap between writing code and delivering value to users.

Summary

Continuous development — encompassing CI, continuous delivery, and continuous deployment — is the foundation of efficient, low-risk software delivery. It is not a single tool or a single step; it is a set of practices that, when layered together, allow teams of any size to ship better software faster. Start with continuous integration, build your test coverage, automate your pipeline incrementally, and your team will spend far less time fighting integration fires and far more time building features your users actually want.

Many practitioners treat DevOps and Agile as competing approaches, but they are more accurately understood as complementary stages on the same spectrum. Agile provides the iterative structure; DevOps provides the operational and tooling layer that automates and sustains that structure at scale. Together, they have become the primary drivers of business automation across industries.

Drawing the Connection Between DevOps and Agile

At its core, DevOps is a development philosophy that seeks to unite product development, testing, and deployment into a single, streamlined workflow. The goal is faster, more reliable delivery of software and services — with fewer handoff delays and less siloed decision-making.

Agile laid the groundwork for this model. As teams adopted Agile practices — sprint planning, daily standups, retrospectives, and incremental releases — they naturally began identifying bottlenecks that automation could address. Manual testing, manual deployments, and manual environment provisioning became obvious targets. DevOps emerged as the framework for automating those steps while maintaining the cross-functional collaboration that Agile encourages.

A practical example: in an Agile software team, each increment of code is tested before it moves to the next stage. DevOps extends this practice by introducing continuous integration (CI) pipelines that automatically run tests every time code is committed. All stakeholders — developers, testers, operations engineers — can see results in real time, catch defects early, and iterate faster.

How Agile and DevOps Drive Business Automation

The combined influence of Agile and DevOps has accelerated automation across three key dimensions: production speed, customer satisfaction, and scalability.

Shortening Production Times

One of the clearest benefits of business automation is reduced cycle time — the interval between identifying a requirement and delivering a working solution. Agile contributes by decomposing complex workflows into discrete, automatable steps. Each step can be handed off to an automated system — a build server, a test runner, a deployment pipeline — without requiring human intervention at every stage.

DevOps complements this by giving operations teams visibility into automated processes through monitoring, logging, and alerting. When an automated stage fails or degrades, the team is notified immediately and can respond before the issue compounds. The net effect is shorter delivery cycles and fewer production incidents.

Improving Customer Satisfaction

Customer expectations evolve rapidly, and businesses that cannot adapt quickly risk losing ground. Automation — guided by Agile and DevOps principles — enables teams to ship updates, fix defects, and respond to feedback on a much shorter timescale than traditional waterfall approaches allow.

Agile’s iterative delivery model means customers receive working increments of a product early and often, rather than waiting for a single large release. DevOps ensures that each increment is tested and deployed consistently, reducing the risk of regressions that erode user trust. Because both methodologies emphasize collaboration across roles — product, engineering, QA, and operations — the end product reflects a broader set of quality and usability inputs.

Building Products at a Larger Scale

Scalability is a prerequisite for automation, and both Agile and DevOps are designed with growth in mind. Agile gives teams a repeatable, measurable process that can be applied to progressively larger and more complex projects. DevOps provides the tooling and cultural practices — infrastructure as code, container orchestration, automated provisioning — that allow teams to scale without proportionally increasing headcount or operational risk.

This combination allows businesses to increase output, enter new markets, and respond to demand spikes without rebuilding their delivery process from scratch each time. Automation is not a one-time investment; it is an ongoing capability that compounds in value as the scale of operations grows.

Key Takeaways

• Agile and DevOps are complementary, not competing. Agile provides the iterative structure; DevOps automates and sustains it.
• Automation reduces human error and shortens cycle times when grounded in the incremental, feedback-driven approach that Agile and DevOps share.
• Continuous integration and continuous delivery (CI/CD) are the most visible technical expressions of this Agile-to-DevOps continuum.
• Customer satisfaction improves when teams can release smaller, higher-quality increments more frequently.
• Scalability requires a repeatable process. Agile and DevOps together provide the framework that makes scaling automation practical.

For engineering teams evaluating where automation can reduce friction, auditing the handoff points between Agile sprints and operational deployment is a practical starting point. Those handoffs — build, test, release, monitor — are where DevOps automation delivers the most immediate and measurable value.

Originally reported by devops.com. Published August 2018.

Overview

SnapLogic announced a set of updates to its Enterprise Integration Cloud platform aimed at bringing enterprise data integration workflows directly into DevOps pipelines. The headline change is native GitHub support, allowing teams to store, share, and version-control Snap connector integrations alongside application code. Additional announcements included Mesosphere DC/OS platform integration, a new service catalog, enhancements to the Iris AI recommendation engine, and improved API monitoring and pipeline search tooling.

GitHub Support: Integrations as Code

The most significant DevOps-facing addition is the ability to store preconfigured integrations—built with SnapLogic’s Snap connectors—directly in GitHub repositories. According to Craig Stewart, Vice President of Product Management at SnapLogic, this change has two main benefits:

• Collaboration across skill levels: Developers at any experience level can share preconfigured integrations via the same GitHub repositories they already use for application code, lowering the barrier to reuse.
• CI/CD pipeline inclusion: DevOps teams can incorporate Snap connectors into continuous integration and continuous delivery (CI/CD) workflows, treating integration configurations as versioned artifacts rather than one-off configurations.

SnapLogic also introduced a Patterns Catalog to make it easier for developers to discover and reuse existing integration patterns. The catalog acts as a searchable library, reducing duplicated effort and encouraging standardization across teams.

Mesosphere DC/OS Integration and the Road to Kubernetes

SnapLogic added support for the Mesosphere DC/OS container orchestration platform, a feature driven by direct customer demand. Stewart noted that this is the first step in a broader container orchestration strategy, with planned support for Kubernetes to follow. At the time of the announcement, SnapLogic characterized Kubernetes as the emerging de facto standard for container orchestration—a prediction that has since proven accurate.

Iris AI Engine Updates: Bidirectional Pipeline Construction

SnapLogic’s Iris AI recommendation engine, which suggests Snap connectors as developers build integration pipelines, received a meaningful update to how it can be invoked:

• Previously: Iris started recommendations from the first Snap connector in the pipeline, working forward.
• New: Start from the last Snap. When a developer places the final Snap on the canvas first, the Integration Assistant now works backward, suggesting preceding Snaps until the pipeline is complete.
• New: Outside-in construction. Developers can place both the first and last Snaps on the canvas, and the Integration Assistant fills in the middle, working from both ends toward the center.

These options give teams more flexibility in how they design pipelines, particularly when the destination system or the source system is well-defined before the intermediate steps are determined.

API Monitoring and Pipeline Search Improvements

Alongside the major feature announcements, SnapLogic enhanced its tooling for operational visibility:

• API monitoring: Updated tools for tracking the health and performance of APIs managed through the platform.
• Pipeline search: Improved search capabilities to help teams locate broken or misconfigured integration pipelines more quickly, reducing mean time to resolution for integration failures.

The Citizen Integrator Model and Enterprise Data Utilization

Stewart framed these updates within a broader SnapLogic strategy around self-service integration. The goal is to enable so-called citizen integrators—non-developer business users within lines of business—to leverage prebuilt integration patterns created by central IT teams, without requiring deep technical expertise.

SnapLogic cited a survey of 500 IT decision-makers conducted by Vanson Bourne on its behalf, which found that organizations were, on average, using only about half the data they collect, and that data influenced fewer than half of business decisions. The survey projected significant spending increases to operationalize data over the following five years.

The practical implication for DevOps teams is that integration is no longer solely an IT concern. As citizen integrators take on more responsibility, the need for reusable, version-controlled, and pipeline-compatible integration patterns becomes a genuine DevOps challenge—one that the GitHub support and Patterns Catalog are directly designed to address.

Key Takeaways

• Snap connector integrations can now be stored and versioned in GitHub, enabling them to participate in standard CI/CD workflows.
• A new Patterns Catalog supports discovery and reuse of preconfigured integration patterns.
• DC/OS support has been added, with Kubernetes support planned as a follow-on.
• The Iris AI engine now supports backward and outside-in pipeline construction, in addition to the original forward-only approach.
• Enhanced API monitoring and pipeline search tools improve operational observability.

SnapLogic Brings Enterprise Integration into the DevOps Toolchain

SnapLogic announced several updates to its Enterprise Integration Cloud platform that move the product closer to standard DevOps practices. The headline addition is GitHub support, which allows integrations built with Snap connectors to be stored, versioned, and shared through GitHub repositories alongside existing application code.

The release also included integration with the Mesosphere DC/OS container platform, a new service catalog, enhancements to the Iris AI recommendation engine, and improvements to API monitoring and broken-pipeline search tooling.

GitHub Support: Sharing and Reusing Snap Connectors in CI/CD Pipelines

Craig Stewart, Vice President of Product Management at SnapLogic, explained that GitHub support lowers the barrier for developers of all skill levels to share preconfigured integrations. Because the integrations live in the same GitHub repositories developers already use for application code, they become first-class citizens in CI/CD workflows.

Practical benefits of the GitHub integration include:

• Version control for integration pipelines: Teams can track changes to Snap connector configurations just as they track source code changes.
• Discoverability through a Patterns Catalog: Preconfigured connectors are surfaced in a central catalog, making it easier for developers to find and reuse existing integration patterns rather than rebuilding them from scratch.
• CI/CD inclusion: Integration pipelines can be embedded directly into automated build and deployment workflows.

This is particularly relevant for organizations pursuing a self-service integration model, where developers across teams need to consume pre-approved integration patterns without requiring deep platform expertise.

Mesosphere DC/OS and Kubernetes Support

SnapLogic added support for the Mesosphere DC/OS platform in direct response to customer demand. At the time of the announcement, the company stated that Kubernetes support was also planned, with the expectation that Kubernetes would become the dominant container orchestration standard.

Container orchestration support signals SnapLogic’s intention to run integration workloads alongside containerized microservices, which is a natural fit as enterprise architectures increasingly rely on distributed, container-based deployments.

Iris AI Enhancements: Building Pipelines from Both Ends

SnapLogic updated its Iris AI-powered Integration Assistant with new ways to construct pipelines. Previously, Iris only offered recommendations starting from the first Snap connector in a pipeline. The update introduced two new construction modes:

• Last-Snap-first mode: A user places the final Snap connector on the canvas, and the Integration Assistant recommends preceding Snaps in reverse order until the full pipeline is complete.
• Outside-in mode: A user defines both the first and last Snaps, and the Integration Assistant fills in the intermediate steps, working inward from both ends simultaneously.

These modes are designed to accommodate different developer mental models — some teams design integrations by starting with the desired output, making the reverse-construction approach more intuitive.

The Business Context: Citizen Integrators and Data Utilization

SnapLogic cited survey results from a Vanson Bourne study of 500 IT decision-makers, conducted on behalf of SnapLogic, to frame the business urgency behind these updates. According to that survey:

• Organizations expected an average 547% return on data investments.
• Revenue increases of an average $5.2 million were anticipated from more effective data use.
• Despite this, organizations were using only about 51% of the data they collected or generated.
• Data influenced fewer than 48% of business decisions.
• Organizations planned to spend an average of $1.7 million over five years to operationalize data — more than double current spending.

SnapLogic’s response to this gap is to enable what it calls citizen integrators — non-IT staff within lines of business who can work with prebuilt integration patterns created by central IT teams. The GitHub-based Patterns Catalog is a direct enabler of this model: IT developers publish vetted integration patterns once, and business-side users discover and deploy them independently.

Why This Matters for DevOps Teams

The core theme across these updates is the convergence of enterprise integration with DevOps discipline. By storing integration pipelines in GitHub, applying AI-assisted construction, and supporting container orchestration platforms, SnapLogic is positioning its platform as a component in modern DevOps pipelines rather than a standalone IT tool.

For DevOps engineers, the practical takeaway is that integration artifacts can now participate in the same version control, code review, and CI/CD workflows as application code — reducing the gap between application development velocity and integration delivery.

As organizations scale DevOps practices beyond software engineering teams into broader enterprise functions, tools that bridge the integration and automation gap without requiring specialist knowledge become increasingly valuable.

But there is a significant assumption embedded in that workflow: that automation plus frequent deployments will, by themselves, drive quality. That assumption only holds if test automation is equally mature and tightly integrated. Without it, teams risk shipping untested code faster than ever before.

The Quality Questions Every CI/CD Pipeline Must Answer

Before a release moves through the pipeline, automated testing must be able to answer the following questions with confidence:

• Correctness: Have the application and code changes been tested thoroughly against expected behaviour?
• Acceptance: Does the release meet the minimum acceptance criteria required for deployment?
• Regression: Will the new release introduce production defects that are hard to debug or disruptive to resolve?
• Performance: Has application performance been evaluated sufficiently under realistic load?
• Security: Has the application been scanned and tested for known security vulnerabilities?

Failing to answer any of these questions in an automated, repeatable way creates blind spots that manual testing alone cannot reliably close at the pace of modern delivery cycles.

What Is Continuous Testing?

To run a genuine CI/CD pipeline, testing must be automated and integrated directly into each stage of that pipeline. Teams that achieve this goal are practising continuous testing.

Continuous testing is not simply running a test suite occasionally or triggering a single smoke test on merge. It means that a curated, prioritised set of automated tests executes automatically at defined pipeline gates — blocking a build from progressing if quality thresholds are not met.

Prerequisites for Continuous Testing

Implementing continuous testing requires investment across three dimensions before it can deliver value inside a pipeline:

• People: Engineers who understand both the application domain and test automation techniques. Quality assurance can no longer be a siloed, post-development activity; it must be a shared responsibility across the development team.
• Practice: Agreed processes for identifying which tests are critical, how acceptance criteria are defined, how failures block or route builds, and how test debt is managed over time.
• Technology: An automation framework appropriate to the application stack, integrated with the CI/CD toolchain so tests run without manual intervention and results are reported in a consumable format.

Aligning Test Automation with Agile Sprints

Agile methodologies introduce a rhythm of short iterations and frequent code changes. Test automation must keep pace with that rhythm rather than lagging behind it. Practically, this means:

• Write tests alongside features: Treat test code as a first-class deliverable within the same sprint as the feature it covers. Deferring automation to a later sprint creates a backlog that compounds quickly.
• Define acceptance criteria as executable tests: Behaviour-driven development (BDD) approaches, such as writing scenarios in Gherkin syntax, help teams translate user stories into verifiable, automated acceptance tests.
• Prioritise test coverage by risk: Not every code path needs the same depth of coverage. Identify the critical paths — authentication, payment flows, data integrity — and ensure those are covered first.
• Keep the test suite fast: A slow test suite discourages developers from running it locally and slows pipeline feedback. Separate long-running integration and performance tests into later pipeline stages so the core unit and component test suite finishes quickly.

Layering Test Types Across the Pipeline

A mature continuous testing strategy maps different test types to appropriate pipeline stages rather than running everything everywhere. A common layering pattern looks like this:

• On commit (pre-merge): Unit tests and fast component tests. These should complete in minutes and provide immediate feedback to the developer.
• On merge to main branch: Integration tests, API contract tests, and a focused set of end-to-end smoke tests that verify the application starts and core user journeys function.
• On deployment to staging: Full regression suite, performance benchmarks, and security scans. These tests may take longer but run in a production-like environment.
• Pre-production gate: Exploratory and acceptance testing, plus any compliance or accessibility checks required before production release.

Common Pitfalls to Avoid

Even teams with automation tooling in place often encounter the same patterns that undermine continuous testing:

• Flaky tests: Tests that pass and fail inconsistently erode trust in the suite. Teams begin ignoring failures, which defeats the entire purpose. Treat flaky tests as bugs and fix or quarantine them promptly.
• Testing only the happy path: Automated tests tend to cover expected behaviour. Edge cases, error states, and boundary conditions are frequently under-tested and are common sources of production defects.
• Neglecting non-functional testing: Performance degradation and security vulnerabilities are rarely caught by functional tests. Integrate lightweight performance and security scanning into the pipeline from the start rather than retrofitting later.
• Treating automation as a one-time project: The test suite requires ongoing maintenance. As the application evolves, tests must be updated or they become a liability rather than an asset.

Measuring the Effectiveness of Continuous Testing

Tracking the right metrics helps teams understand whether their continuous testing practice is improving or stagnating. Useful indicators include:

• Defect escape rate: The proportion of defects found in production versus those caught earlier in the pipeline. A declining escape rate indicates improving test coverage.
• Mean time to detect (MTTD): How quickly the pipeline identifies a defect after a failing code change is introduced.
• Test suite duration: Slow pipelines reduce deployment frequency. Tracking suite execution time highlights where optimisation is needed.
• Flaky test rate: The percentage of test runs that produce inconsistent results. A rising flaky rate is an early warning sign of quality debt.

Summary

A CI/CD pipeline without integrated test automation is a fast path to production — but not necessarily a reliable one. Continuous testing is the practice that closes that gap. It requires teams to invest in the right people, establish agreed practices for test ownership and acceptance criteria, and select tooling that fits naturally into the pipeline. When done well, continuous testing shifts quality assurance left, surfaces defects early when they are cheap to fix, and gives teams the confidence to deploy frequently without sacrificing reliability.

RAD—a form of continuous development—creates an efficient pipeline for moving changes from development into production. As software complexity grows and deployment targets multiply (on-premises servers, public cloud, hybrid environments), the build-and-deploy process introduces new risks. DevOps tools, designed to orchestrate application configuration and automate deployments, can address those risks. But when RAD meets DevOps automation, the most common sticking point is the mismatch between test environments and production environments.

Development and testing typically occur in sandboxes that bear little resemblance to actual production infrastructure. That makes it difficult—sometimes impossible—to accurately test deployment and update scenarios until late in the development cycle, often not until pilot or user-acceptance testing.

Building a Better RAD Model

A modern RAD model that is genuinely ready for deployment must satisfy three conditions:

1. Protected but realistic sandboxes. Development must occur in an environment isolated from production, yet closely mirroring production infrastructure and configuration.
2. Shared tooling across all stages. The DevOps tools used for production deployment must also support RAD testing flows at every earlier stage.
3. Operations feedback injected early. Operational knowledge—about configuration, provisioning, failure modes—must inform development practices from the start, not just at release time.

Theoretically, enterprises could build a series of production-mirror sandboxes—each with equivalent hosting or cloud resources—for every development and testing stage. That would produce accurate tests and faster iteration cycles. In practice, however, maintaining such large-scale, consistent replica environments is expensive and operationally complex. It also introduces risk: a misconfigured boundary between a staging sandbox and production can allow untested code to reach real users.

Cloud infrastructure is both a primary deployment target for RAD applications and a viable platform for hosting development-as-a-service sandboxes. Even so, most enterprises cannot sustain a full series of consistently provisioned, cloud-hosted sandboxes that faithfully mirror production end-to-end. A different approach is needed.

Using Containers to Bridge RAD and DevOps

Containers—and container orchestration—directly address all three conditions above. By packaging an application with its dependencies and runtime configuration, containers reduce the differences between development sandboxes, test environments, and production. A containerized application behaves consistently regardless of the underlying host, which is precisely what a reliable RAD pipeline requires.

Adding containers to an application opens two pathways for uniting DevOps with RAD:

• Integrate DevOps tooling with container-based RAD. Extend an existing DevOps pipeline to support container deployments at every stage, using plug-ins or extensions available in major DevOps tools.
• Integrate container orchestration directly into RAD tools. Essentially replace traditional DevOps configuration management with a container orchestration platform, treating the RAD pipeline itself as the authoritative framework.

The right choice depends on what is already in place. If an organisation has a mature DevOps toolchain, adapting that pipeline for containers may be less disruptive. If the organisation is starting fresh, or if most software is developed in-house and iterated rapidly, building around RAD and container orchestration tools from the start may be the cleaner path.

Imperative vs. Declarative Automation

Traditional DevOps configuration management tools are rooted in how to configure an application rather than where or when to deploy it. Many of these tools are imperative (script-based), which makes them awkward to use in a configuration-independent, container-centric manner. Declarative automation tools—where you define the desired end state rather than the sequence of steps—are more naturally compatible with container orchestration. When a declarative tool invokes container orchestration through a plug-in, the same automation model can be applied consistently across every stage of the development pipeline, from unit testing through production.

Container Orchestration and CI Tools in Practice

An increasing number of organisations adapt their RAD tooling rather than reworking their entire DevOps toolchain. Container orchestration platforms such as Kubernetes—and historically Marathon on Apache Mesos—combine readily with continuous integration tools such as Jenkins, Buildbot, and CruiseControl. Together, they make every stage of rapid development—from unit testing through integration testing and into production—part of an explicit, auditable pipeline.

In this model, each pipeline stage is defined in RAD terms and links to a container orchestration layer for deployment. Simply running application code in containers does not automatically guarantee that orchestration practices will be consistent across stages—that still requires deliberate design. However, containers and a single orchestration tool provide a common foundation that spans the entire pipeline, ensuring that every transition—including the move to production—is tested and repeatable.

Choosing the Right Foundation

Whether you adapt your DevOps pipeline for containers or rebuild around RAD and container orchestration tools, the RAD model itself is a sequence of testing and integration phases linked together. The right starting question is: what is the central framework?

• If development is pipeline-centric—most software is built in-house and iterated rapidly—base the integration on the RAD pipeline and the tools that manage it. Container orchestration becomes the deployment mechanism that the pipeline calls at each stage.
• If deployment is the primary concern—most software is procured rather than developed internally, and the challenge is reliably configuring and provisioning diverse applications—then a DevOps configuration-automation foundation makes more sense, extended with container support where applicable.

In either case, the key principle is consistency: the same tooling, the same orchestration model, and the same environmental assumptions should apply from the earliest development sandbox through to production. Achieving that consistency is what transforms a theoretical RAD-DevOps partnership into a pipeline that actually reduces deployment risk.

Source: This article is based on analysis originally published on TechTarget.com.

Why Organisations Are Adopting DevOps

The pressure to release software more frequently—while simultaneously improving quality—is the primary driver behind DevOps adoption. Traditional siloed structures, where development throws code over the wall to operations, cannot sustain the pace modern markets demand. DevOps addresses this by aligning people, processes, and tooling around a shared goal: delivering working software reliably and repeatedly.

Key business motivators include:

• Faster time to market: Continuous integration and delivery pipelines shorten the cycle from code commit to production deployment.
• Higher release frequency: Smaller, more frequent releases reduce the risk associated with each deployment and make rollbacks easier.
• Improved software quality: Automated testing embedded throughout the pipeline catches defects earlier and at lower cost.
• Shorter recovery times: Robust monitoring and automated rollback mechanisms reduce mean time to recovery (MTTR) when incidents occur.
• Cultural alignment: Breaking down silos between development, QA, and operations fosters shared ownership of quality and reliability.

Organisations such as Amazon, Netflix, Target, and Walmart are frequently cited as examples of companies that have scaled DevOps practices to achieve competitive advantage—though the specifics of their implementations vary significantly by team and product line.

The “You Build It, You Run It” Principle

One of the most significant cultural shifts in a mature DevOps organisation is the adoption of the you build it, you run it model. Development teams take on operational responsibility for the services they ship. This changes incentives: engineers who must respond to 3 a.m. pages for their own code write more observable, more resilient software. It also removes the handoff delay between development and operations, accelerating the feedback loop from production back to engineering.

Implementing this model requires investment in tooling (observability platforms, on-call rotation management, runbooks) and a genuine organisational commitment to reducing toil and improving reliability over time.

Building Your DevOps Roadmap: Step-by-Step

A DevOps roadmap is not a single project plan—it is an evolving guide that shapes culture, processes, and technology choices over months and years. The following steps provide a practical framework for getting started.

1. Assess Whether DevOps Is the Right Fit

Before committing resources, evaluate your current delivery pain points honestly. Are slow releases caused by process bottlenecks, skill gaps, tooling deficiencies, or organisational structure? DevOps addresses specific problems; if your core issue is unclear requirements or underfunded infrastructure, those must be tackled alongside—or before—a DevOps initiative.

2. Break Down Organisational Silos

Silos between development, QA, security, and operations are the single largest impediment to DevOps success. Structural changes—such as embedding QA engineers and security specialists within product teams, establishing shared on-call responsibilities, and creating cross-functional working groups—are necessary preconditions for a functioning DevOps culture. No amount of tooling compensates for teams that do not communicate.

3. Centre the Customer in Every Decision

DevOps investments should be traceable to improved end-user outcomes. Define success metrics that reflect customer experience—deployment frequency, change failure rate, lead time for changes, and MTTR are widely used indicators (popularised by the DORA research programme). Use these metrics to prioritise roadmap initiatives and demonstrate business value to leadership.

4. Start Small, Then Scale

Attempting to transform an entire organisation simultaneously is a common and costly mistake. Choose a pilot team or a non-critical service, implement CI/CD and automated testing there first, measure the results, and use those learnings to build the case for broader adoption. Incremental progress sustains momentum and reduces risk.

5. Automate Relentlessly

Automation is the engine of DevOps. Priority targets for automation include:

• Build and test pipelines: Every code commit should trigger an automated build and a suite of unit, integration, and regression tests.
• Infrastructure provisioning: Infrastructure-as-code tools (Terraform, Ansible, Pulumi) make environment creation repeatable and auditable.
• Deployment: Automated deployment pipelines with built-in approval gates reduce human error and deployment lead time.
• Security scanning: Static analysis (SAST) and dependency vulnerability scanning should run automatically on every pull request.

6. Select Compatible, Integrated Tooling

The DevOps toolchain spans source control, CI/CD, containerisation, orchestration, monitoring, and more. Common choices include Git-based platforms (GitHub, GitLab, Bitbucket), CI/CD engines (Jenkins, GitHub Actions, GitLab CI, CircleCI), container runtimes (Docker), orchestration (Kubernetes), and cloud providers (AWS, Azure, GCP). Tool selection should be driven by team familiarity, integration capability, and long-term maintainability—not marketing trends. Avoid tool sprawl: a small, well-integrated set of tools outperforms a large, fragmented one.

7. Implement Continuous Integration and Continuous Delivery

CI/CD is the operational backbone of DevOps. Continuous Integration ensures that every developer’s changes are merged, built, and tested frequently—ideally multiple times per day. Continuous Delivery extends this to ensure the software is always in a deployable state, with production releases triggered on demand. Continuous Deployment goes one step further by automating production releases after all automated gates pass. The appropriate model depends on your organisation’s risk tolerance and regulatory environment.

8. Establish Real-Time Visibility and Monitoring

You cannot improve what you cannot observe. Implement centralised logging, distributed tracing, and metrics collection across all services. Alerting should be actionable—noisy alerts breed alert fatigue and mask real incidents. Dashboards should surface the health of the delivery pipeline as well as production system performance, giving both engineering and business stakeholders a shared understanding of system state.

9. Build Robust Feedback Loops

Fast feedback is what distinguishes high-performing DevOps organisations. This means short-lived feature branches, pull request reviews completed within hours rather than days, automated test results surfaced immediately in the developer’s workflow, and production metrics visible to the team that built the feature. Post-incident reviews (blameless retrospectives) are a critical feedback mechanism for operational reliability.

Addressing DevOps Complexity and Security

As DevOps practices mature, complexity grows. More services, more pipelines, and more infrastructure mean more moving parts to manage and secure. Two areas deserve particular attention:

Managing Tool and Pipeline Complexity

Organisations with sprawling toolchains often find that visibility across the entire delivery pipeline becomes difficult. Platform engineering teams—dedicated groups that build and maintain the internal developer platform—have emerged as a response to this challenge. By standardising the delivery platform and abstracting complexity from application teams, platform engineering reduces cognitive load and improves consistency across the organisation.

Integrating Security: Shifting Left

Security cannot be bolted on at the end of the pipeline. The DevSecOps approach integrates security practices—threat modelling, dependency scanning, SAST, secrets management, and compliance checks—into the CI/CD pipeline from the beginning. Finding and fixing vulnerabilities in development is dramatically cheaper than addressing them in production. Organisations supporting hybrid cloud infrastructure have the additional challenge of maintaining consistent security policies across on-premises and cloud environments, which requires careful tooling and governance decisions.

Measuring Progress and Iterating

A DevOps roadmap is a living document. Regularly review your DORA metrics (deployment frequency, lead time for changes, change failure rate, MTTR), solicit feedback from engineering teams about friction in the delivery process, and adjust priorities accordingly. Cultural and process changes take longer to show results than tooling changes—set realistic expectations with leadership and celebrate incremental wins to sustain organisational buy-in.

Summary

Implementing a successful DevOps roadmap requires more than installing a CI/CD tool. It demands deliberate cultural change, a commitment to automation, disciplined tooling choices, and continuous measurement. Organisations that treat DevOps as a long-term capability investment—rather than a one-time project—are the ones that realise its full benefits: faster delivery, higher quality, improved reliability, and teams that are genuinely empowered to innovate.

Testing Has Not Changed — But DevOps Demands More of It

Ben Riley opened his talk on continuous testing with a simple observation: “Everyone in this room has tested something.” The fundamentals of testing — defining an idea, writing requirements, and validating behaviour — have remained largely unchanged for 30 years. What has changed is the pace at which software must be delivered, and the volume of changes that must be validated.

At Nationwide Building Society, the formal DevOps journey began roughly 18 months before the summit, though Richard Jordan noted that informal DevOps practices had been in place for at least five years. The key insight from their experience: adopting DevOps did not reduce the need for testing. It increased it. Continuous testing became the primary method of measuring delivery progress.

Two Testing Models: Ice Creams vs. Volcanoes

Riley and Jordan used two memorable shapes to describe contrasting testing philosophies:

The Ice Cream Model (Traditional)

The traditional testing pyramid is often described as an inverted cone — wide at the top and narrow at the bottom. Riley and Jordan call this the “ice cream” shape. It is characterised by:

• A large base of manual UI and integration testing
• A small proportion of automated unit tests at the narrow end
• Slow feedback cycles because manual testing dominates
• Poor repeatability and scalability at the UI layer

Traditional manual UI testing is inherently slow and difficult to run consistently at scale. When defects are found late in the cycle, they are significantly more expensive to fix.

The Volcano Model (Automation-First)

The “volcano” is the ice cream flipped upside down. The wide base represents a large suite of automated unit tests, with progressively smaller layers of:

• Unit tests — the vast majority, fast and cheap to run
• Component tests — validating individual services or modules in isolation
• API and integration tests — verifying interactions between components
• GUI tests — limited automated checks at the interface layer
• Manual session-based testing — the narrow peak, reserved for exploratory and edge-case scenarios

This approach encourages development teams to write and own their automated tests — “quick to build, stable to run” — so that failures surface early, when bugs are fast and cheap to fix. The guiding principle is fail fast and fail early.

Why the Volcano Model Suits DevOps

Continuous integration and continuous delivery pipelines depend on rapid, reliable feedback. A test suite dominated by slow manual steps creates a bottleneck that undermines the entire DevOps value stream. The volcano model aligns directly with DevOps principles:

• Speed: Automated unit and component tests run in seconds or minutes, not hours or days.
• Repeatability: Automated tests produce consistent results across environments, reducing flakiness and human error.
• Shift-left: Pushing testing responsibility toward developers and earlier pipeline stages means defects are caught before they compound.
• Scalability: Thousands of automated tests can run in parallel on every commit; manual testing cannot scale the same way.

The Cultural Challenge: Getting Developers to Test

Moving from ice cream to volcano is not purely a tooling problem — it is a cultural one. At Nationwide, Jordan encountered pushback from developers who were now being asked to write and maintain their own automated tests, a responsibility that had traditionally belonged to a separate QA team.

Jordan’s approach combined incentive and expectation:

• Carrot: Offering developers new, modern testing tools that make the work less painful
• Stick: Being clear that the old model is no longer viable and that this change is non-negotiable

Embedding an automated-testing mindset from the start of a project — rather than retrofitting it — is essential. At the same time, Nationwide’s team was actively addressing technical and design debt that made legacy systems harder to test automatically.

There Is No Big Bang — Only Incremental Progress

Nationwide’s transition away from the ice cream model was, by Jordan’s own admission, far from complete at the time of the summit. “There won’t be a big bang, but we’ll chip away at it,” he said. This is a realistic and honest assessment that most organisations will recognise.

Shifting a testing culture in a large financial institution takes time. Legacy test suites, existing toolchains, and established team habits do not change overnight. The practical advice from Riley and Jordan is to make incremental improvements: replace the next manual test with an automated one, instrument the next new service from day one, and steadily move the balance of effort from the top of the ice cream to the base of the volcano.

Key Takeaways

• Automated testing is not a nice-to-have in DevOps — it is the feedback engine that makes continuous delivery possible.
• The “volcano” model (automation-heavy at the unit level, manual testing reserved for exploratory work) is better suited to DevOps pipelines than the traditional manual-heavy “ice cream” model.
• Shifting testing left means developers must own automated tests. This requires both cultural change and the right tooling support.
• Incremental progress beats waiting for a perfect solution. Start automating the next test, not all tests at once.
• Technical debt and testability debt must be addressed together, or automated coverage will remain limited.

Why Enterprises Are Betting on DevOps

A benchmarking study by the Hackett Group, titled Beyond World-Class Service: IT’s Digital Capability Imperative, identified DevOps as a key enabler of digital transformation. The report concluded that “agile, DevOps and continuous development models are well-suited to digital business transformation, which demands accelerated development, rapid prototyping and incremental value.”

Supporting evidence comes from a Forrester survey of 4,976 IT professionals, which found that firms with high-performing business technology solution delivery organisations were 1.5 times more likely than their peers to exceed profitability, market share and productivity goals.

European adoption is already significant. A survey of 750 IT decision-makers conducted for Claranet found that 29% of European businesses had already adopted a DevOps approach at the time of publication, with a further 54% expecting to do so within two years. Respondents cited better applications (54%), greater operational efficiency (53%), and improved customer satisfaction (45%) as the primary drivers.

Despite this momentum, translating DevOps intent into tangible outcomes is proving harder than many organisations anticipated. Three recurring challenges dominate enterprise conversations: broken delivery pipelines, difficulties scaling DevOps culture, and acquiring the right skills.

Fixing a Broken DevOps Pipeline

“There is an understandable rush to implement DevOps as the promise of delivering technology faster and more efficiently is compelling,” says Tim Russell, chief product officer at Perforce. “The immediacy of action often drives quick-fix or easiest-path solutions that, in turn, drive significant yet unintended downstream costs and efforts, even if it seems as though improvements are visible in parts of the process.”

Chris Hill, software development manager at Jaguar Land Rover (JLR), described this challenge in a video interview with Computer Weekly filmed at DOES 2017 in London. “A poor software development lifecycle is slower than it needs to be, has roadblocks and a slow path to production,” he said. If a developer cannot clearly see how to get code into production, or the path is convoluted, features are simply not released as quickly as they could be.

JLR faces a constraint that is common in embedded-systems industries: when putting software into vehicles, the team does not have the relative simplicity of a web deployment model. Spinning up a fleet of physical vehicles to run automated test suites is not feasible, so the team relies on virtualisation and software-based infrastructure to build code that is representative of the production vehicle’s operating environment. This is a reminder that some technology constraints are genuinely immutable and cannot be resolved purely through cultural change.

Legacy IT presents a related obstacle. Justin Dean, senior vice-president for technical operations at Ticketmaster, described at the Cloud Native Computing Forum how DevOps principles can still apply to teams managing legacy software stacks. At Ticketmaster, the team that writes software also carries operational duties. A key part of Ticketmaster’s DevOps transformation involved giving those teams access to the technical environments needed to deploy software independently.

One practical approach Ticketmaster adopted is a formalised work pipeline: tasks requiring legacy team involvement are submitted through a structured queue. Once completed, the DevOps team proceeds with deployment. Dean’s assessment of containerised delivery was direct: “When you deliver software in a container through a pipeline, it forces a massive change. This single-handedly had more impact than anything else and dovetailed into DevOps.”

The Challenge of Scaling a DevOps Culture

Culture remains one of the most cited barriers to successful DevOps adoption. While DevOps is often framed as a software development and IT operations concern, sustained success requires a shift in mindset across the wider business — including product, finance, and senior leadership.

BBVA, the multinational banking group, has applied DevOps at scale across multiple regions as part of a broader transformation programme. Speaking at DOES 2017, Brian Timmeny, head of advanced engineering, DevOps and IT processes at BBVA, acknowledged that “when you scale DevOps at more than a team level, some things can become more prescriptive.” His advice to large enterprises: start with a simple, clear message — “you are going to achieve speed and you are going to achieve quality.”

With around 140,000 employees and approximately 12,000 developers, BBVA’s transformation had to involve the business directly. The bank began by working to define what a “BBVA product” actually means internally. “One of the key tenets of DevOps is for a team to have end-to-end responsibility for the product,” said Timmeny. “You have a team that will build it, deploy it and monitor it — they will own it, cradle to grave.”

The difficulty is that large organisations are typically structured by function, not by product. Timmeny’s practical solution was to establish truly self-sufficient internal teams first, then gradually wrap a business function around each team. “This gives us a place to start — to have a set of core principles that, to begin with, can be done in a minimal viable way. Over time, everything else will evolve.”

This approach required top-down executive support, which was available at BBVA in part because the financial sector broadly recognises the urgency of technological transformation. Without visible leadership commitment, scaling DevOps culture across a large enterprise is likely to stall.

Getting the Right Skills

Hiring external experts to close a DevOps skills gap can seem like the fastest solution, but Forrester cautions against relying too heavily on external recruitment. Its report Organise and Staff I&O Pros for Successful DevOps Practices noted that DevOps role vacancies rose by 15% between December 2015 and December 2016, pushing salaries up — while also increasing the risk of candidates who have “DevOps washed” their CVs to appear more experienced than they are.

Forrester principal analyst Amy DeMartine articulated the broader skills requirement: “The success of the integrated product team relies on the ability to pull expertise from all resources required to create a successful digital solution. Resources from application development and IT and operations professionals need expertise from security professionals, customer experience professionals, QA, and enterprise and business architecture professionals. The success of these teams is measured by meeting the customer experience goals.”

Tom Clark, head of common platform at ITV and a DevOps advocate, offered a more direct hiring framework when speaking to Computer Weekly at DOES 2017. “As technology has moved forward, the skills you require have moved up. But at ITV we have boiled it down to two qualities: you have to be smart and you have to be kind. Anything else is a bonus.”

At ITV, “smart” means the capacity to adapt to change — since today’s tooling will not be tomorrow’s. “Kind” means the ability to integrate into and strengthen a team. Clark put it plainly: “Don’t be a douche, just be nice.”

Key Takeaways for DevOps Practitioners

• Audit your pipeline first. Visible improvements in one part of a delivery pipeline can mask deeper bottlenecks downstream. Map the full path to production before selecting tools or restructuring teams.
• Legacy integration needs a formal interface. A structured work-queue model between legacy and cloud-native teams, as used at Ticketmaster, can reduce friction without requiring a full legacy re-platform.
• Define what a “product” means in your organisation. DevOps team ownership is meaningless without a shared, business-level definition of the product a team is responsible for.
• Scale culture top-down and bottom-up simultaneously. BBVA’s experience shows that executive sponsorship is necessary but not sufficient — self-sufficient teams must also be built from the ground up.
• Screen for adaptability, not just current toolchain knowledge. Given how rapidly the DevOps toolchain evolves, the ability to learn matters more than familiarity with any specific tool.
• Be wary of CV inflation. With DevOps salaries rising and demand outstripping supply, skills validation during hiring is essential.

The challenge is especially acute when support is outsourced. In-house teams at least have direct lines to developers and operations staff who can act as second- or third-level support. A third-party help desk operating at arm’s length from the DevOps pipeline faces a much steeper information gap. Closing that gap requires deliberate strategy across metrics, communication, tooling, and incentives.

Why Traditional Help Desk Metrics Fail in a DevOps Environment

Most support contracts are built around metrics designed for stable, infrequently-changing systems: time-to-close, number of calls answered, and simple resolution rates. These KPIs made reasonable sense under waterfall or long-release-cycle delivery models. They break down when releases ship weekly, daily, or on-demand.

The deeper problem is that many common KPIs are easy to game in ways that hurt users:

• Calls answered: An agent can pick up and immediately drop a call, satisfying the metric while leaving the user no better off.
• Time to resolution: Agents can mark tickets resolved before the user has confirmed the issue is fixed. The user then has to re-open or raise a new ticket, inflating ticket volume and eroding trust.
• One-size-fits-all SLAs: Requiring all issues to be resolved within one working day is meaningless. A password reset is often too urgent to wait a full day; a complex root-cause investigation should not be rushed to hit an arbitrary deadline.

The first step in building a DevOps-ready support model is to audit existing KPIs, eliminate the ones that can be gamed or that don’t reflect actual user outcomes, and replace them with metrics that measure real value.

Integrating the Help Desk into the DevOps Pipeline

Support cannot be an afterthought bolted onto the end of a release process. It must be a first-class participant in the DevOps loop. That means keeping support staff continuously informed about what is changing, when it goes live, who it affects, and why it matters to the business.

Early Access and Canary Participation

One of the most effective practices is giving support staff early access to new functionality during the test phase — before changes reach the full user base. In a mature DevOps pipeline, code is often deployed to a small subset of users first (sometimes called a canary release). Part of the support team should be included in that group so they can discover edge cases, usability issues, and common gotchas before the phones start ringing.

Rotating the early-access group ensures that knowledge spreads across the entire support team over time rather than concentrating in a few individuals.

Skills-Based Routing

When only a subset of the support team has previewed a given change, skills-based routing becomes essential. Incoming requests related to a new feature or deployment should be directed to agents who are already up to speed on that change. Routing a call about a new release to an agent who has no context on it wastes time and damages user confidence.

Business-Priority Classification

Not all incidents have equal business impact. Allow users to indicate how severely an issue is affecting their ability to work. When the support team identifies clusters of the same issue — multiple users reporting similar problems — those clusters should automatically escalate in priority regardless of individual ticket age. This surfaces systemic problems quickly and ties support activity directly to business impact.

Building Feedback Loops Between Support and Development

Feedback loops are foundational to DevOps. A user-reported issue is a signal that something in the codebase or deployment needs attention. That signal has to reach developers quickly — ideally before the next deployment cycle makes the underlying code even harder to change.

Legacy ticketing systems that operate in isolation from development tooling create delays and information loss. Modern DevOps platforms integrate incident tracking with change management, so a support ticket can be linked directly to a code commit, a deployment event, or a backlog item. Investing in that integration reduces the lag between a user complaint and a developer action.

Developers naturally tend to prioritize issues by technical interest. User feedback — especially quantified impact scores — provides a corrective signal, anchoring prioritization decisions in business value rather than engineering preference.

Measuring Support Outcomes That Actually Matter

Replace gamed or irrelevant KPIs with metrics that reflect genuine user outcomes and support team effectiveness in a high-velocity environment:

• User-confirmed resolution: A ticket should only be marked resolved when the user confirms it — not when the agent decides it is. Send simple, low-friction reminders (a single-click confirmation) to close the loop without burdening the user.
• Expectation-matched SLAs: Set and communicate realistic resolution timeframes based on issue type. Tell users upfront: a password reset happens during the call; a complex integration bug will have a status update within one hour. Then hold the team to those commitments.
• User satisfaction surveys: Post-resolution surveys provide qualitative signal that raw ticket metrics miss. Analyse trends to identify bottlenecks between the help desk and development or operations teams.
• Repeat-contact rate: If users are raising the same issue multiple times, earlier resolutions were superficial. Tracking repeat contacts exposes false closures that simple resolution-rate metrics hide.

Making Outsourced DevOps Support Work

Outsourced support arrangements add contractual and organizational distance to an already complex problem. A few principles help make them work:

• Reward collaboration, not just compliance: A purely penalty-driven contract encourages finger-pointing when things go wrong. Build in positive incentives — financial rewards for both the outsourcing partner and internal staff — when issues are resolved efficiently and users report high satisfaction.
• Share the DevOps roadmap: Outsourced teams need the same advance visibility into upcoming changes that in-house teams need. Contractually require regular briefings on planned deployments and their expected user impact.
• Define escalation paths clearly: When an outsourced agent cannot resolve an issue, the path to second- and third-level support (including internal DevOps team members) must be fast and well-documented. Ambiguous escalation paths are one of the most common failure modes in outsourced support models.
• Align tooling: Where possible, give outsourced support teams access to the same incident and change management platforms used internally. Siloed tooling fragments the feedback loop.

Key Takeaways

DevOps and effective help desk support are not inherently in conflict — but they require deliberate alignment. The practices that make the biggest difference are:

1. Audit and replace KPIs that can be gamed or that ignore user-confirmed outcomes.
2. Include support teams in early access and canary deployments so they are informed before users call.
3. Use skills-based routing to match incoming requests to agents who know the relevant changes.
4. Invest in tooling that connects support tickets directly to the development and deployment pipeline.
5. Let users — not agents — mark issues as resolved, and survey them afterward.
6. Build incentive structures that reward outsourced and in-house teams for collaboration and genuine resolution quality.

A DevOps strategy that ignores the help desk is incomplete. The support function is where the consequences of every deployment decision become visible to the business. Treat it as a core part of the feedback loop, not an afterthought, and the entire delivery pipeline becomes more resilient and more responsive.

The DevOps Tooling Challenge

Organisations are moving away from traditional waterfall and cascade software development — where code is produced over defined time periods and handed off between teams — toward more agile, iterative approaches. The demand for rapid delivery via continuous integration (CI), continuous development, and continuous delivery (CD) has pushed many businesses to adopt DevOps practices.

DevOps aims to bring together development, testing, and operations teams to streamline how code moves through the software delivery pipeline. Done well, it delivers frequent, reliable releases with minimal disruption. Done poorly — especially when tooling is left ungoverned — it creates fragmented environments that are difficult to audit, support, or scale.

Developer-Led Tool Adoption: Bottom-Up Growth

One of the defining characteristics of DevOps tooling adoption is that it tends to grow from the bottom up rather than being driven by top-down mandates. Because many DevOps tools are open source and freely downloadable, individual developers and system administrators often adopt them without any formal procurement process or organisational approval.

Developers look for tools that integrate easily with the environments they already use — commercial IDEs such as IBM Rational Software Architect or Microsoft Visual Studio, open source IDEs such as Eclipse or Anjuta, and project management tools such as Atlassian Jira or CA PPM.

This organic adoption has driven widespread use of tools such as:

• Jenkins — An open source automation server that manages software versioning and supports continuous delivery pipelines. It integrates with version control systems including Git, Subversion, Perforce, CVS, and Accurev via its extensive plugin ecosystem.
• Chef — A configuration management tool originally focused on the operations side of DevOps, used to define infrastructure as code and automate provisioning.
• Puppet — Another configuration management system that enforces desired infrastructure state and automates repetitive operational tasks.

These tools began as point solutions targeting specific roles or workflow stages. Over time — Puppet having been released around 2005, Chef around 2009, and Jenkins in 2011 as a continuation of the Hudson project started at Sun in 2004 — their capabilities have expanded significantly and increasingly overlap.

An Expanding Ecosystem of Options

Jenkins, Chef, and Puppet are no longer the only significant players. The broader DevOps tooling ecosystem now includes a wide range of open source and commercially supported options:

• Ansible and Salt — Agentless or lightweight configuration management and orchestration tools that compete with Chef and Puppet in many environments.
• Docker — Container runtime and image management tooling that has become foundational to modern application packaging and delivery.
• Kubernetes — A container orchestration platform backed by the Cloud Native Computing Foundation (CNCF), with member organisations including AWS, Google, Microsoft, IBM, Intel, and Twitter. Kubernetes has rapidly become a dominant force in managing containerised workloads at scale.
• Cloud Foundry — An open source platform-as-a-service with strong automation capabilities and broad integration support for upstream and downstream systems. It goes beyond containerised environments and has a wider application runtime reach than Kubernetes alone. The KuBo project (Kubernetes on BOSH) provides an integrated Cloud Foundry and Kubernetes stack for heterogeneous environments.
• HashiCorp tools (Terraform, Vagrant) — Terraform enables infrastructure provisioning as code across multiple cloud providers; Vagrant simplifies the creation and management of reproducible development environments.
• Perforce Helix — A portfolio of DevOps tools that extends beyond Perforce’s version control origins to cover a broad range of delivery pipeline functions.

Canonical also offers an enterprise-supported Kubernetes distribution via its Juju tooling, providing an Ubuntu-integrated option for teams standardising on that ecosystem.

The Problem of Too Many Tools

When individual engineers freely adopt tools of their own choosing, organisations can quickly end up with a fragmented toolchain — multiple incompatible systems, no unified visibility, and significant gaps in audit trails and feedback loops. Managing this situation typically follows one of two approaches:

Approach 1: Prescriptive Standardisation

Mandate a single approved toolset and require everyone to use it. In theory, this creates consistency and simplifies support. In practice, it frequently fails. Developers and system administrators are resourceful; if a tool they prefer is banned, they tend to continue using it informally while outwardly complying. The result is a false sense of control — organisations believe they have standardised their environment, only to discover fragmented tool usage when something breaks and a postmortem is conducted.

Approach 2: Pragmatic Integration

Accept that ad hoc tool adoption has already occurred and focus on creating an integration layer that brings structure to the existing mix. Most mature open source DevOps tools now expose open APIs or support plugin architectures that allow them to interoperate with other tools at a reasonable level of fidelity. This makes it feasible to build an orchestration or governance layer on top of a heterogeneous toolchain rather than attempting to eliminate it.

Where stricter controls and enterprise support are required, commercially supported distributions of open source tools can provide the additional reliability and accountability organisations need. CloudBees, for example, offers enterprise support and additional components built on top of Jenkins.

Open Systems and Long-Term Flexibility

When evaluating DevOps tooling — particularly umbrella or orchestration platforms — openness should be a primary criterion. The tooling landscape changes rapidly. Kubernetes did not exist a decade ago; tools that are central to many pipelines today may be superseded within the next five years. If an overarching platform is tightly coupled to specific underlying tools, organisations risk being locked into a vendor’s roadmap and unable to adopt newer, more capable alternatives as they emerge.

Platforms that support plugin-based or API-driven integration allow teams to swap underlying tools as the ecosystem evolves. This architectural flexibility is a key factor in long-term viability.

Several commercial platforms aim to provide this kind of open orchestration layer:

• CA Automic — Offers automated DevOps process orchestration designed to work with a range of underlying tools in an open manner.
• BMC BladeLogic — An automation suite rooted in traditional systems management that has been modernised to support cloud and container environments. When combined with other BMC products (Atrium Orchestrator, Control-M, TrueSight), it can provide broad DevOps workflow coverage.
• HPE OneView — Bridges the hardware/software boundary by enabling logical platform configuration and provisioning of physical, virtualised, or containerised software workloads.

Cloud-Native DevOps Options

Major cloud providers have invested heavily in native DevOps tooling that removes the need to self-manage infrastructure for CI/CD pipelines:

• AWS — Offers CodePipeline, CodeBuild, CodeDeploy, and related services integrated with its broader cloud platform.
• Google Cloud — Provides Cloud Build and related services, with strong Kubernetes integration through GKE.
• Microsoft Azure — Azure DevOps (formerly VSTS) provides end-to-end pipeline management tightly integrated with the Azure ecosystem.
• IBM Cloud (formerly BlueMix) — Offers a broad set of DevOps-related services and toolchain integrations.

Most of the open source tools discussed in this article are also available for self-hosted deployment on these cloud platforms, giving teams flexibility in how they manage their toolchains.

Key Takeaways for DevOps Engineers

• Governance matters. Ungoverned tool proliferation creates invisible technical debt and audit gaps. Establish visibility into what tools are in use before mandating what should be.
• Pragmatism beats prescription. Attempting to enforce a single toolset by fiat rarely succeeds in engineering organisations. Focus instead on integration layers and shared standards.
• Openness is a non-functional requirement. Evaluate any orchestration or CI/CD platform on its ability to integrate with a range of tools via open APIs or plugins — not just its current feature set.
• Business requirements drive tooling, not the other way round. DevOps tooling should ultimately serve business outcomes: faster delivery of reliable software. Tool selection should follow from that goal, not precede it.
• The landscape will keep changing. Invest in architectural flexibility — the ability to swap tools as the ecosystem evolves — rather than optimising for any specific tool combination that is popular today.

The organisations that get the most out of DevOps are those that apply rigorous thinking to their toolchain strategy: providing umbrella systems that impose order on existing complexity, while giving engineers the autonomy to use the tools that make them most effective.

The Speed vs. Security Tension in DevOps

DevOps teams operate under relentless pressure to ship faster, iterate more frequently, and deliver higher-quality software. Speed-to-market has moved from competitive differentiator to baseline expectation. To meet that expectation, teams have embraced agile methodologies, containerisation, microservices architectures, and public cloud services.

Public cloud in particular has become a cornerstone of DevOps workflows, giving teams the ability to provision infrastructure on demand without waiting for traditional IT procurement cycles. However, that velocity comes with a cost: security and compliance controls are frequently deferred, deprioritised, or bypassed entirely when teams are racing to hit a release deadline.

Why Security Falls Through the Cracks

Most enterprises adopt DevOps from the bottom up — at the project, team, or business-unit level — rather than through a coordinated, company-wide initiative. This decentralised adoption model means that individual DevOps teams often spin up cloud resources without going through central IT or security channels. The result is a classic shadow IT problem at cloud scale.

When security and compliance are treated as a final gate before release rather than as an integral part of the development pipeline, several problems compound:

• Late-stage security findings introduce bugs and cause significant release delays.
• Security teams lose visibility into the infrastructure landscape because hundreds or thousands of changes can be pushed to production daily.
• Breaches and compliance violations carry costs far beyond the immediate incident — lost revenue, reputational damage, and erosion of customer trust all follow.
• Friction between teams escalates when security reviews are perceived as blockers rather than enablers.

The Case for Automated, Continuous Security

The answer to the speed-vs-security dilemma is not to slow down DevOps — it is to embed security directly into the pipeline through automation. A continuous security and compliance model mirrors the continuous integration and continuous delivery (CI/CD) philosophy that DevOps teams already rely on for code quality.

Automation means that security policies are defined as code, enforced programmatically, and applied consistently across every environment and every deployment — without requiring a manual review checkpoint that halts the pipeline. Cloud-native security platforms can leverage the API-centric architecture of public cloud providers to monitor configurations, detect drift, enforce guardrails, and alert on violations in real time.

Who Benefits from Continuous Security Automation?

A well-implemented automated security and compliance platform creates advantages for every stakeholder in the organisation.

Developers

Developers gain earlier feedback on security risks — catching vulnerabilities during development rather than during a pre-release audit. Pre-approved, security-hardened infrastructure templates allow teams to spin up new environments quickly without reinventing security controls for each project. Over time, the platform becomes a learning tool, helping developers internalise secure coding and infrastructure practices.

Security Teams

Security teams can shift from a reactive, firefighting posture to a proactive one. Instead of struggling to audit what infrastructure exists after the fact, they can define policy centrally and have it enforced automatically across all DevOps workloads. Continuous monitoring provides a real-time view of the organisation’s cloud security posture, making it possible to detect and respond to anomalies quickly rather than discovering them during an incident.

The Organisation as a Whole

At the organisational level, automated continuous security reduces the risk of costly breaches and compliance failures while accelerating — not slowing — software delivery. The chronic tension between DevOps and security teams diminishes when both sides are working within a shared, automated framework rather than against each other at release gates.

Key Practices for Implementing Security Automation in DevOps

Adopting a continuous security model requires both tooling and cultural change. The following practices provide a practical starting point:

1. Shift security left. Integrate static analysis (SAST), dependency scanning, and secrets detection into the CI pipeline so developers receive feedback before code is merged, not after it is deployed.
2. Define security policy as code. Use tools such as Open Policy Agent, Checkov, or cloud-provider-native policy frameworks to express compliance rules in version-controlled code that can be reviewed and tested like any other software artefact.
3. Enforce infrastructure guardrails. Use pre-approved infrastructure templates (Terraform modules, AWS Service Catalog entries, etc.) that have security controls baked in, reducing the chance that developers inadvertently misconfigure cloud resources.
4. Implement continuous compliance monitoring. Deploy runtime monitoring tools that detect configuration drift, exposed storage buckets, over-permissive IAM roles, and other common cloud misconfigurations as soon as they occur.
5. Automate remediation where possible. For well-understood, low-risk violations, configure automated remediation (for example, automatically enforcing encryption on an unencrypted storage bucket) to reduce the burden on both developers and security teams.
6. Build shared dashboards. Give both DevOps and security teams visibility into the same compliance and risk metrics. Shared visibility fosters shared ownership.

Conclusion

Public cloud and DevOps practices together unlock extraordinary speed and flexibility. But without deliberate attention to security, that speed also accelerates the accumulation of risk. Automated, continuous security and compliance — built on policy-as-code, runtime monitoring, and pre-approved infrastructure patterns — allows DevOps teams to maintain their velocity while giving security teams the visibility and control they need. The result benefits developers, security practitioners, compliance officers, and the business as a whole: faster delivery, fewer surprises, and a measurably stronger security posture.

Under this model, a product can reach the market with a focused initial feature set, then grow incrementally to meet validated user demand — a pattern that routinely proves less wasteful than discovering, late in a long development cycle, that core assumptions were wrong.

Why Iterative Delivery Is Becoming the Default

The mobile ecosystem has had an outsized influence on user expectations. Consumers now take it for granted that the apps on their phones will receive meaningful updates on a weekly or even daily basis. That rhythm is resetting expectations for enterprise software too.

Umit Tacay, CTO of ANATAS, put it plainly: “The mobile industry has impacted software delivery quite a bit. Mobile apps are being delivered quickly and they’re getting updates quickly. That’s setting users’ expectations in general. So IT is going to have to change the way it works to be delivering iterative, quick updates, smaller applications and adding features, removing features, much more quickly than what they were used to.”

This shift is not optional for teams that want to remain competitive. The question is not whether to adopt more iterative delivery, but how to build the organisational muscle to sustain it.

Continuous Delivery Is an Organisational Discipline, Not a Tool

A common mistake is treating continuous delivery as a purely technical problem — something solved by adopting the right CI/CD platform and moving on. In practice, the tooling is the easy part. The hard part is changing the habits, incentives and processes of everyone involved in building and operating software.

Tacay cautioned: “It is a discipline that requires a lot of change to the way people throughout an organisation work. If you Google how to produce an app then attempt it, you might push out one project. But you are not going to build the discipline that’s necessary to enable you to deliver continuously — and that is the whole point of this exercise.”

Sustainable continuous delivery touches code review culture, testing practices, deployment authority, on-call responsibilities, funding models and executive decision-making. Getting a single team to deploy daily is a proof of concept. Getting an entire organisation to do so reliably is a transformation programme.

The Legacy Application Problem

Many large organisations — particularly in regulated industries — still depend on core systems that are a decade or more old. These systems present a compounding challenge: maintenance costs rise over time, the programming languages and frameworks they use fall out of mainstream use, and the specialists who can work on them become scarcer and more expensive to hire or retain.

Matt Mansell, Chief Strategy Officer at IntegrationQA, described the dynamic with a useful analogy: “It’s like when you try to keep your 30-year-old car functioning without maintaining it very well for those 30 years. It just gets harder and harder and more and more expensive. Parts get harder to find.”

The implication is clear: delay makes legacy migration more difficult, not less. Organisations that postpone modernisation to avoid short-term disruption typically face a larger, more disruptive transition later — and a narrowing window in which the institutional knowledge needed to execute it safely still exists.

A Four-Step Framework for Continuous Organisational Change

Moving from a legacy delivery model to one built around continuous delivery requires a structured change management approach. The following four-step framework — drawn from the ANATAS consulting methodology — provides a repeatable path from assessment through ongoing delivery.

Step 1: Conduct a Maturity Assessment

Before planning any changes, establish a clear and honest picture of where the organisation stands today. A maturity assessment covers the current state of IT infrastructure, governance structures, technology strategy, and how work is funded and prioritised. Without this baseline, improvement efforts lack a reference point and roadmap milestones become meaningless.

Step 2: Define the Target State with Key Stakeholders

Collaborate with decision-makers across the business — not just the IT department — to articulate what success looks like. This step surfaces conflicting priorities early, aligns leadership around shared goals, and ensures the target state reflects genuine business outcomes rather than technology preferences alone. Stakeholder involvement at this stage also builds the buy-in needed to sustain change through inevitable friction.

Step 3: Prepare a Transition Road Map

With a current state and a target state defined, the next step is mapping the viable pathways between them. A good road map identifies dependencies, sequencing constraints, quick wins that build momentum, and longer-horizon initiatives that require sustained investment. It should also account for risk — particularly around systems that cannot afford downtime — and set realistic expectations about pace.

Step 4: Deliver Continuously and Evolve the Road Map

The road map is not a fixed plan — it is a living document. Execution happens through an ongoing cycle of experimentation and feedback, consistent with the same iterative principles that continuous delivery applies to software. As the organisation learns what works, the road map is updated. Wins are consolidated, assumptions are challenged, and the target state itself may shift as the business environment changes.

Key Takeaways

• Continuous delivery is an iterative approach to software development that reduces risk by shipping smaller, more frequent changes.
• User expectations — shaped by the mobile app economy — are driving demand for faster, more responsive software updates across all sectors.
• Sustainable continuous delivery requires organisation-wide change, not just new tooling in the engineering team.
• Legacy systems become progressively harder and more expensive to replace the longer modernisation is deferred.
• A structured four-step framework — assess, align, road map, and continuously deliver — provides a practical path for organisations making this transition.

This article draws on source material originally published by anatas.com.au and incorporates perspectives from Umit Tacay (CTO, ANATAS) and Matt Mansell (CSO, IntegrationQA).