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Category: Continuous Delivery

Continuous delivery is a software engineering discipline that allows organizations to consistently develop and release quality software at a rapid pace. It is achieved by automating the software development lifecycle, enabling tasks such as development, testing and release to occur concurrently and continuously.

  • Using Observability to Create a Smart Data-Driven Culture

    Using Observability to Create a Smart Data-Driven Culture

    The first words that come to mind when talking about smart technologies are often artificial intelligence, machine learning and automation. While these smart technologies play a crucial role in smart-driven cultures, they are not the sole factor in making workplaces “smart.” Organizations that have a smart-driven culture, or data-driven culture, also promote innovation. An innovative organization actively encourages creativity, exploration and the pursuit of new ideas, continuously developing and implementing groundbreaking solutions, products and processes. Innovative companies with a culture of learning and growth are often seen as desirable employers and have high employee retention rates, attracting top talent.

    Let’s go over the benefits of a smart-driven culture and how an observability platform can help your company become data-driven in five steps.

    Why is Smart-Driven Culture Important

    You can increase efficiency and productivity among your employees by fostering a smart-driven culture. Smart technologies are able to automate repetitive tasks and streamline processes, which helps free up workers’ time and focus on other issues instead.

    A data-driven culture can also drastically improve decision-making on a company level. Your organization will begin to strategize and act on objective insights rather than guesswork or assumptions. Through collecting, analyzing and interpreting data, you will not only be able to make informed decisions but also know how to optimize processes and mitigate risks.

    Use data to better understand your customers’ preferences and improve your products to meet their demands more effectively. For example, leverage smart technologies and create high-tech touchpoints to tailor each of your customer’s experiences. A culture that encourages continuous learning, experimentation and adaptation is ready to navigate market shifts, changing customer needs and emerging technologies.

    How to Create a Data-Driven Culture in Five Steps

    According to the 2023 Data Analytics Survey conducted by New Vantage Partners, 79.8% of data leaders identified people, business processes and organizational alignment as the principal challenge to becoming data-driven. These folks cited cultural issues, including organizational receptivity to change, communication and organizational alignment, as the greatest obstacles to realizing business value.

    One way to overcome these challenges is by harnessing the power of observability, building data literacy and organizational transformation. Observability refers to the practice of gaining real-time insights into systems, applications, and processes through the collection and analysis of relevant data. Real-time insights will enable your organization to make data-driven decisions, understand your customers better and quickly identify areas of optimization to improve your products.

    These characteristics are fundamental to smart-driven companies, enabling them to stay competitive and drive innovation in the market. Take the following five steps to ensure you’re maximizing the potential of observability and paving the way toward becoming a smart-driven organization:

    1. Define Clear Goals

    Start by defining what a smart-driven culture means for your company. Write down clear outcomes that you want to achieve with a new culture, and identify the specific areas where observability can make a significant impact on your team.

    Focus on areas where real-time insights derived from observability can generate substantial business value. Set SMART (specific, measurable, achievable, relevant and time-bound) goals that can be tracked over time. For example, you could aim to reduce website loading time by 20% in the next two months in order to increase customer conversion.

    2. Implement Robust Observability Tools

    Evaluate and choose observability tools that align with your organization’s specific needs and goals. Look for tools that offer features such as real-time monitoring, log management, distributed tracing, advanced analytics and visualization capabilities.

    Consider factors like scalability, ease of use, integration with existing systems, and the ability to handle diverse data sources. Remember that the availability of accurate and timely insights promotes a culture that values data and encourages performance for achieving business objectives.

    Ask your IT department to help set up your observability tools to gather the relevant data. For our previous example goal of improving website loading times, this will mean gathering real-time data on website performance, such as server response times, page load speeds, and user interactions.

    3. Promote Data Literacy

    Your observability tools are of little help if no one understands how to use and derive helpful insights from the data you gather. Educate all of your employees on good data practices and the role of analytics in decision-making.

    Provide training and support on the data tools you have chosen and encourage your employees to understand, interpret and utilize data effectively in their day-to-day work. For instance, in meetings, you can ask for data points to back up business decisions.

    4. Encourage Collaboration and Knowledge Sharing

    Creating cross-functional teams and encouraging collaboration among different departments, such as IT, operations and analytics, is crucial for the successful implementation of observability practices.

    Taking an interdisciplinary approach allows for a better understanding of the systems and data within the organization. This can lead to the identification of optimization opportunities, help break data silos and practice collective problem-solving as a result.

    5. Iterate and Improve

    Embrace a culture of experimentation and continuous improvement by using observability data to identify areas for optimization and innovation. Encourage teams to test hypotheses, implement changes and measure the impact, fostering a cycle of learning and refinement. Track progress against the goals you set at the beginning and adjust your trajectory if needed.

    Coming back to our goal to improve website loading time. Now that your interdisciplinary team implemented some changes and reduced the loading times, you can assess the impact on user experience and business metrics, such as bounce rates, conversion rates and customer satisfaction.

    Becoming a smart-driven organization will grant you a great competitive advantage. You will make better and faster decisions, employ the best talent, maximize their productivity, and provide an enhanced experience for your customers.

  • Google’s Shiny New AI Dev Environment — the ‘Experimental’ Project IDX

    Google’s Shiny New AI Dev Environment — the ‘Experimental’ Project IDX

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Google’s full-stack, browser based development environment in the cloud. It’s not just a Copilot clone, but aims to help you “get an app from zero to production.” (more…)

  • How New Tech Elevates Release Management’s Quality Standards

    How New Tech Elevates Release Management’s Quality Standards

    Release management is a critical discipline within software development and IT operations that focuses on the planning, scheduling, coordination and deployment of software releases. It includes the procedures, plans and methods employed to guarantee that software updates or items are sent to customers in a secure and effective way. Release management’s main objective is to make it possible for software releases to be delivered reliably and smoothly while causing as little disturbance as possible for the company and its users. This entails overseeing a release’s whole lifespan, from preliminary planning through testing, deployment and post-release support.

    Challenges in Traditional Release Management

    Traditional release management approaches often face several challenges that can hinder the efficient and effective delivery of software releases. These include manual and time-consuming processes, lack of visibility and traceability, complex and fragmented environments, limited collaboration and communication and a lack of effective communication channels and collaboration tools. Manual processes can be time-consuming, error-prone and lead to delays in release cycles. Lack of visibility and traceability can make it difficult to track the status of releases, identify bottlenecks and troubleshoot issues effectively. Fragmented environments can be complex and differ widely from one another, making managing and synchronizing these environments manually challenging and prone to errors.

    Limited collaboration and communication can lead to miscommunication, delays and misunderstandings. Traditional release management can lead to silos between development, operations and other stakeholders, hindering efficient coordination. Inconsistent release practices can lead to variations in processes, documentation and quality standards, making it difficult to establish a standardized and repeatable release process. Lack of automation and continuous integration can slow down the release process, increase the chances of human errors and inhibit the ability to deliver frequent, small and reliable releases. Limited scale and adaptability can be addressed by adopting new technology and modernizing release management practices. Automation, continuous integration, deployment pipelines, collaboration tools and standardized processes can help overcome these hurdles and enable more efficient, reliable and quality-driven release management.

    Benefits of New Technology in Release Management

    Advanced Testing Techniques for Improved Quality Assurance

    1. Enhanced speed and efficiency
    2. Improved interaction and collaboration
    3. Improved stability and quality of software
    4. Reduced risk and downtime

    Using New Technology: Implementation Techniques

    1. Organizational readiness assessment
    2. Choosing the correct technologies and tools
    3. Developing an all-inclusive release management strategy
    4. Embracing the DevOps and Agile principles

    The Impact of New Technology on Release Management Quality

    Automation: Accelerating Release Cycles and Ensuring Consistency

    Automation plays a pivotal role in release management by significantly accelerating release cycles. It reduces manual effort and expedites the delivery of software releases, leading to a faster time-to-market. Automated workflows enforce standardized practices, configurations and environments, resulting in more reliable and predictable releases. Automated testing frameworks and tools enable faster and more comprehensive testing, while continuous integration and automated build processes facilitate seamless integration of code changes. Automation also enables better visibility and traceability, allowing release management teams to track the progress of releases, identify bottlenecks and analyze performance metrics.

    Continuous Integration: Early Detection of Issues and Improved Collaboration

    Continuous integration has revolutionized software development practices by emphasizing early issue detection and enhanced collaboration. By regularly integrating their code, businesses can spot problems early and fix them, decreasing the possibility of significant issues later in the development cycle. Additionally, increased developer collaboration encourages better coordination, communication, and information sharing, which results in a more effective and unified development process.

    Organizations can profit from continuous integration by utilizing automated build processes, extensive test suites and a collaborative development environment. Early problem identification and enhanced teamwork not only lead to better software quality but also enable development teams to build applications more quickly and consistently. Organizations can achieve improved efficiency, productivity and success in the dynamic world of software development by adopting continuous integration as a core practice.

    The Role of Innovation in Breaking Barriers: Future Outlook and Trends

    Machine Learning and Artificial Intelligence in Release Management

    Release management is not an exception to the way that machine learning (ML) and artificial intelligence (AI) are transforming numerous sectors. Technologies based on machine learning and artificial intelligence (ML and AI) provide useful capabilities that can significantly improve release management procedures, increase effectiveness, and promote improved software quality. The function of ML and AI in release management is described in more detail below:

    Automated Testing and Quality Assurance:
    In release management, ML and AI may automate and enhance testing and quality assurance procedures. Intelligent algorithms can examine enormous amounts of data to find trends, anomalies and potential issues in brand-new software releases. To cut down on testing time and effort while maintaining high standards, ML models can be trained to anticipate and prioritize test cases. AI-driven testing solutions can also suggest the right test coverage and offer insights into the effects of code modifications.

    Predictive Analytics for Release Planning
    ML and AI approaches can be used to create prediction models for release planning using past release data, such as metrics, logs and performance statistics. These models are able to predict potential dangers, predict release dates, and allocate resources more efficiently. Release managers may prioritize important tasks, make data-driven decisions and improve the planning process for software releases by employing AI-driven insights.

    Anomaly Detection and Continuous Monitoring
    ML and AI algorithms can continuously monitor the production environment, analyzing real-time data to spot anomalies and potential problems. Release management teams can proactively identify and respond to performance bottlenecks, security breaches, and other key occurrences by putting in place sophisticated monitoring solutions. A faster and more reliable reaction to production issues can be achieved with the use of ML-powered anomaly detection, which can decrease the mean time to detect (MTTD) and mean time to resolve (MTTR).

    Impact Analysis and Risk Assessment for Releases
    ML and AI approaches can be used to evaluate the hazards that may be involved in a release. ML models can estimate the effect of a release on several areas like performance, stability and user experience by assessing historical data, code modifications, dependencies, and environmental factors. Release managers can prepare mitigation methods, make educated decisions, and reduce the likelihood of unforeseen problems throughout the release process thanks to these risk assessment skills.

    Intelligent Release Deployment and Rollback
    ML and AI technology can help with intelligently choosing when to deploy and roll back a release. In order to recommend deployment tactics, machine learning (ML) models can learn from previous deployment experiences, including success rates, user input and mistake patterns. In order to ensure a more dependable and error-resistant deployment process, AI algorithms can track the deployment process, evaluate the system’s health and initiate automated rollbacks if anomalies or severe concerns are discovered.

    In general, ML and AI provide valuable capabilities to release management, boosting testing and quality assurance, streamlining release planning, enabling continuous monitoring, analyzing release risks and simplifying wise deployment decisions. Utilizing these technologies enables businesses to improve software quality, streamline release management procedures, and deliver high-performing applications more effectively and reliably.

    Microservices Architecture and Cloud-Native Technologies
    With the advent of cloud-native technologies and the adoption of microservices architecture, release management has been transformed, enabling organizations to leverage the benefits of scalability, agility, and flexibility. Let’s explore how cloud-native technologies and microservices architecture enhance release management:

    Containerization and Orchestration
    Release management has a solid foundation thanks to cloud-native technologies like containerization (like Docker) and orchestration frameworks (like Kubernetes). Application components are encapsulated in containers, which makes them very portable and independent of the underlying infrastructure. Release management can use containerization to streamline application packaging and distribution while ensuring consistent application deployment across a variety of environments. Frameworks for orchestration allow for automated container deployment, scaling and administration, speeding up release management procedures.

    Scalability and Elasticity
    Applications can use cloud-native technologies’ inherent scalability and elasticity to dynamically modify resources in response to demand. The modular and independent services of the microservices architecture make granular scalability possible. Release management can use these features to scale individual services or components on their own, ensuring optimal resource usage and effective handling of increasing user loads. This adaptability reduces downtime and enhances the application’s general performance during releases.

    Fault Isolation and Resilience
    Failures in one service do not affect the entire system, thanks to the fault isolation and resilience that the microservices architecture fosters. Release management can use this resilience to lessen the impact of any potential issues during releases. It is possible to roll back or replace a specific service if it develops issues without affecting other services. Release management may efficiently manage errors, improve fault tolerance and maintain service availability during the release process by implementing cloud-native technologies and microservices.

    Observability and Monitoring
    The enhanced observability and monitoring capabilities made possible by cloud-native technology allow for release management. Platforms for container orchestration include integrated monitoring and logging capabilities that give information on the efficiency of an application’s use of resources and the state of the system overall. With the use of this information, release management teams can proactively monitor releases, spot abnormalities and quickly resolve problems. Observability and real-time monitoring support the release process by ensuring the dependability and stability of programs.

    Release management greatly benefits from cloud-native technologies and microservice architecture. Organizations can improve their dependability, achieve faster and more frequent releases, achieve scalability, fault isolation, and observability, and produce high-quality software applications by leveraging containerization, orchestration, CI/CD methods and many other technologies. Organizations are better equipped to manage complex release cycles and adjust to the changing needs of the software industry when they adopt these cutting-edge methods.

    Conclusion

    In this article, we have explored new technology’s role in raising the bar for release management quality standards. Organizations may increase communication, optimize processes, and more quickly produce high-quality software by adopting automation, continuous integration and DevOps approaches. The advantages and revolutionary potential of new technology in release management are apparent, notwithstanding the risks and difficulties that may arise. Organizations may promote continuous improvement, adjust to market demands and succeed in today’s changing business environment by tearing down barriers and embracing innovation.

  • 2024—Year of the Linux Desktop? ChromeOS Reflects its Inner Penguin ¦ GNOME Rethink

    2024—Year of the Linux Desktop? ChromeOS Reflects its Inner Penguin ¦ GNOME Rethink

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Can the Linux desktop installed base ever break the mythical 10% barrier? Google has been refactoring ChromeOS, and GNOME is working on new window manager ideas. (more…)

  • Digital.ai Embraces Multiple AI Classes to Advance DevSecOps

    Digital.ai Embraces Multiple AI Classes to Advance DevSecOps

    Digital.ai has committed to adding generative artificial intelligence (AI) and additional machine learning capabilities to its namesake platform for managing DevSecOps workflows.

    The company plans to make available in alpha a generative AI tool to create test cases and user stories via large language model (LLMs) access via a natural language interface. In addition, generative AI will also be used to identify high-risk changes to code and improve the overall governance of DevSecOps workflows.

    At the same time, Digital.ai has extended the machine learning algorithms it already provides to provide predictions and scoring. Additional capabilities include flow acceleration recommendations, change risk predictions and service management process optimizations.

    Digital.ai CEO Derek Holt said as AI continues to evolve, the company will use a mix of public and private LLM platforms to ingest AI into DevSecOps workflows. In effect, the future of AI will almost be multimodal as different classes of AI technologies are used to automate workflows, he added.

    The goal is to safely break down the silos of data that today make it difficult to cost-effectively build and deploy secure software without compromising the rate at which it is developed, noted Holt.

    It’s still early days as far as the infusion of AI in workflows is concerned, but it’s apparent that many of the manual tasks that prevent organizations from fully embracing best DevSecOps practices will be automated.

    At the same time, increased reliance on digital processes, coupled with more stringent pending regulations, are reducing the overall tolerance organizations have for insecure software. The only way to address that rising sense of application security urgency—at a time when more complex cloud-native applications are being built and deployed—is to rely more on automation. In fact, the race is now on to secure software supply chains before regulations that will have stiff penalties for security lapses go into effect.

    Most of the AI advances in software development that have been made thus far benefit developers. However, many of those same core AI advances will soon be applied to software engineering. Many of the DevSecOps bottlenecks that exist today will start to fade away as various classes of algorithms are able to both identify and make recommendations to eliminate them. As those capabilities evolve, it should also serve to help reduce the long-standing cultural divide between application development teams and cybersecurity professionals that is at the root of the software supply chain issues that organizations are still struggling to resolve.

    In fact, many organizations would be well-advised to review their current workflows with an eye toward determining which processes can be automated. The days when AI was sparingly used are now giving way to a new era of software development that promises to dramatically accelerate the rate at which applications can be built, deployed and updated. The challenge, as always, is finding a way to incorporate these advances into workflows that were not created with AI in mind.

  • The DevOps Pendulum: Agility Vs. Control

    The DevOps Pendulum: Agility Vs. Control

    Managing changes to cloud assets is a universal pain felt by many engineering leaders today, despite all of the advancements in tooling and practices like GitOps. This is because, in reality, it is simply impossible to always have everything completely locked down—we don’t live in a utopia with zero incidents. If your engineering organization prevents any changes from being made through the cloud console or to your infrastructure-as-code (IaC) without complying with strict GitOps practices or change management processes via CI/CD, then it’s likely you have very frustrated developers—developers who can’t troubleshoot or debug in real-time and who have little control in a real-world incident.

    Engineering, like everything else in life, is all about balance.

    The Pendulum Swings

    The pandemic created a whole new mindset and practice for managing distributed and high velocity engineering and operations. Overnight, companies that were not built for remote ways of working had to continue their operations in a global, distributed and asynchronous way that they weren’t completely familiar with. This required a new way to think about software delivery—and it accelerated the DevOps practices that support that delivery. Self-service infrastructure removed barriers for developers to ensure continued performance and velocity.

    At the same time, your cloud cannot be the wild west with everyone creating bespoke infrastructure. It becomes impossible to manage, and misconfigurations can be risky. Guardrails and policy automation have become a hot topic. Today, with tech markets depressed and cloud costs escalating, there seems to be a growing trend toward locking things down again, even at the risk of frustrating developers.

    This begs the question: How can you achieve unencumbered infrastructure for your developers while simultaneously following policies and best practices for compliance, risk and cost? There is a way to find balance.

    Like many aspects of security, we’ve learned that when constraints and barriers are too high, users ultimately find ways to bypass them. This is true for operations, as well. While sometimes it might seem easier to lock everything down than design a better and more balanced way to enable developers to move fast, eventually this approach backfires. This is the exact same evolution the application and cloud-native security industry are undergoing right now.  All the guardrails and controls applied have created too much friction in development processes and developers ultimately bypass them.

    Real-Time Insight is Key

    CloudOps can learn a lot from the disruption the security industry is going through today. In the same way that point-in-time security has been rendered completely useless, non-real-time alerting or eventual alerting to infrastructure drift just won’t cut it when managing an ephemeral cloud. What is really needed is the same kind of real-time and continuous scanning of cloud assets and IaC, similar to what we apply to our systems through monitoring and observability. These solutions became an essential backbone of our business to ensure continuous operations and availability of cloud services.

    As we embrace IaC and the benefits it brings, everything-as-code unlocks greater agility and visibility, enabling you to automatically remediate without locking everything down. As DevOps says, “fail forward and fail fast.” Rather than focusing on never making a mistake, focus on how to fix it immediately. 

    The Solution

    By providing a continuous comparison of actual cloud assets with their desired state through IaC and GitOps, it’s possible to surface configuration drift and policy violations immediately, much like any other kind of breach or major system failure. Failures and incidents are inevitable. It’s unrealistic, and even dangerous, to build systems with an inherent underlying design that prevents you from changing something at the cloud console at 2:00 A.M. during an outage.

    With a locked-down approach, a developer would need to wait for the change management approval during a high-pressure production incident, oftentimes at 3:00 A.M. (because the incident gods of always make them happen in the middle of the night) or on a weekend. We don’t need to look much further than the catastrophic 40-minute and $440 million Knight Capital incident to understand that sometimes time is not on our side and that a delay can have severe consequences.

    Cloud drift has become equally troubling as uptime or any other mission-critical aspect of continuous business operations, and that’s why we need to apply the same principles for monitoring drift in real-time as we do to our CPU and load. This will make it possible to be alerted when your cloud and your IaC are no longer aligned and even flag issues that can be prevented in real-time. You can automate ticket creation and escalation and also triage whether this is something that needs to be fixed in real-time with remediation suggestions provided or open a ticket to fix it later. The choice should be yours, not made by an administrator who has a lot less context about the systems and the ultimate business impact.

    It is Not ‘One and Done’

    Some vendors preach codifying your cloud into IaC and then locking it down so that you’ll never have to do it again. This feels like deja vu; we’ve definitely heard this before. It sounds a lot like the ‘write once, deploy anywhere’ JVM promise that never quite panned out. 

    By going down this path, you’re resigning yourself to never adding new cloud assets or changing your cloud configurations. If there’s one thing we’ve learned about the cloud, it’s that it’s a constantly moving target.  (Read what cloud asset management can learn from the world of finance—another industry in constant flux). If this didn’t work decades ago with the JVM, it certainly won’t work in a dynamic and constantly changing environment like the cloud. That’s a fool’s journey.

    Continuous Cloud Up to Code

    There are major downsides of locking down agility and velocity. This is hard-earned advice, witnessed firsthand from tools like legacy CMDB and startups who have forgotten that cloud is ephemeral—and possibly the reason why we all use it in the first place! So if our cloud is constantly changing and is a continuously moving target, then shouldn’t our tools be built in just the same way to keep up? Solutions built for the rapidly evolving cloud-native era must continuously look for new, unmanaged and changed assets and ensure their cloud infrastructure always follows policies and regulatory standards.  

    Once we are able to find the balance between agility and control, we’ll be able to tap into the benefits of speed and safety (as DORA research shows time and again) that are the benchmark of high-performing teams. Automating cloud asset management can provide governance, policies and control while still enabling the velocity and agility engineering organizations today require. 

    Agility Versus Control

    There’s a minority of people who claim they don’t worry about cloud asset configuration drift because they have everything completely locked down. Zero changes can be done at the cloud console or to IaC without going through GitOps for strict change management processes via CI/CD. I bet they have happy developers—not!

    Life is All About Balance

    Throughout the pandemic, organizations had to make developers happy for fear they would leave. So DevOps went full tilt aiming for self-serve infrastructure for developers to remove barriers to speed and velocity. Now, with tech markets depressed, maybe it’s easier to lock things down even if that risks losing engineers.

    As we embrace IaC and its benefits, you need not live in the stone age of locking everything down. How can you achieve unencumbered infrastructure for your developers while following policies and best practices for compliance, risk and cost? There is a way. 

    The solution? Real-time continuous scanning of your cloud assets and your IaC with continuous comparison can surface configuration drift and policy violations immediately. Emergencies are inevitable. It’s bold/stupid/foolish to think you’ll never need to change something at the cloud console during an emergency or that a developer will wait for change management when production is down at 3:00 A.M. on a weekend. It happens, and you must deal with it. 

  • Automation in DevOps: Advancements, Platforms and Examples

    Automation in DevOps: Advancements, Platforms and Examples

    Automation is the backbone of DevOps. By facilitating seamless integration and continuous delivery, it revolutionizes the ways organizations deliver software, products and services. When applied correctly, automation can significantly enhance productivity, reduce time-to-market, improve software quality and minimize the risk of human error.

    A range of automation tools support these goals, each tailored to a specific aspect of the software development life cycle (SDLC).

    From code integration to testing and deployment, these tools optimize processes and enable the rapid and reliable release of software. In this article, we will explore the role of automation in the DevOps landscape.

    The Importance of Automation in DevOps

    Automation in DevOps is the process of designing, developing and deploying software and systems with minimal human intervention. The goal is to streamline the software development life cycle from design, development and testing to deployment and management.

    Automation provides several benefits:

    1. Speed and efficiency: Automation enables quick development and deployment of software, which results in faster delivery of features and fixes to customers.
    2. Reliability: Automated systems are less prone to human error, resulting in more reliable code deployment and management.
    3. Consistency: Automation guarantees that routine tasks are performed the same way every time, ensuring consistent results.
    4. Reduced cost: Automation eliminates the need for manual intervention in routine tasks, leading to a reduction in operating costs.
    5. Increased productivity: By taking over routine tasks, automation frees up the team to focus on more critical, high-level tasks.

    Automation in DevOps: An Overview

    Automation in DevOps encapsulates various stages, from development and testing to integration, deployment, and monitoring. The process is often referred to as a DevOps pipeline, a set of practices aimed at reducing development times and improving software quality.

    ● Development: Automation begins at the development phase with tools for source control (like Git), which help developers manage and track code changes.
    ● Integration: The next step is continuous integration, where tools like Jenkins automate the build process, allowing teams to identify issues early in the development life cycle.
    ● Testing: Automated testing tools (like Selenium) streamline the testing process, ensuring that the code is error-free and ready for deployment.
    ● Deployment: Continuous deployment tools (like Kubernetes) automate the deployment process, providing fast, consistent and repeatable deployments.
    ● Documenting: Technical documentation is automated with tools like Scriversi to ensure updated, error-free documentation through each version and release.
    ● Monitoring: Finally, automated monitoring tools (like Prometheus) offer real-time insights into the application’s performance, facilitating proactive troubleshooting.

    Automation’s main objective is to minimize manual intervention, thus reducing the risk of errors, improving efficiency and fostering a culture of shared responsibility.

    Examples of Automation in DevOps

    1. Continuous Integration/Continuous Deployment (CI/CD): CI/CD is a critical element of DevOps automation. With CI, developers merge their changes back to the main branch as often as possible. The CI/CD server automatically builds and tests every change. For example, Jenkins, a popular open source automation server, allows developers to automate various stages of their build pipeline.

    2. Infrastructure-as-Code (IaC): This practice involves managing and provisioning computing infrastructure with machine-readable script files rather than physical hardware configuration or interactive configuration tools. For instance, Terraform, an open source IaC software tool, provides a consistent CLI workflow to manage hundreds of cloud services.

    3. Configuration Management: This is the process of maintaining a system’s configuration in a known, consistent state. For example, Ansible, a configuration management tool, helps to automate software provisioning, configuration management and application deployment.

    4. Monitoring and Logging: Automated monitoring and logging systems gather data on how software and hardware are performing and alert the team to any problems. One example is Prometheus, a popular open source monitoring and alerting toolkit.

    5. Technical Documentation: Proper documentation is crucial in the software development process. Yet, it is often overlooked or not updated frequently due to time constraints or the tedious nature of the work. When automated, it simplifies and automates the process of creating and maintaining technical documents.

    Final Thoughts

    In the world of DevOps, automation is not a luxury but a necessity. By automating repetitive and error-prone tasks, teams can focus on what they do best: creating innovative solutions that meet customer needs. From CI/CD, IaC and configuration management to monitoring and logging, automation simplifies the software development process, reduces human error and enhances speed and efficiency. Tools like Jenkins, Terraform, Ansible, Prometheus and Scriversi support this trend, providing various automation capabilities that make the DevOps approach more efficient and effective.

  • Extending the GitOps Pipeline: DevSecOps and Trusted Application Delivery

    Extending the GitOps Pipeline: DevSecOps and Trusted Application Delivery

    We live in a fast-paced world of rapid changes and uncertainty where every aspect of our daily lives changes often, including the way we work and communicate. Organizations must deliver value to their customers at a much faster pace than ever before to thrive in such an environment. However, the decentralized nature of cloud infrastructure expands the threat landscape and poses several security risks that enterprises should address in light of recent data breaches.

    Organizations must combat security threats and address challenges arising from cloud misconfigurations. Several recent tools, strategies, practices and technologies have emerged that can help you handle such security threats. This article discusses how the fusion of DevSecOps and trusted application delivery can extend the GitOps pipeline and add business value.

    What is GitOps?

    Coined by Weaveworks in 2017, GitOps thrives on a source code management system built on Git and adheres to the belief that Git is the single and definitive source of truth. Since all changes are stored in Git, anyone can see the audit trail if necessary. This enables you to use Git-based version control to manage your application’s code, environment and processes.

    Figure 1: CI/CD with Git-based Version Control

    GitOps leverages Git repositories as the one and only source of truth and embraces DevOps and infrastructure-as-code (IaC) best practices. Using GitOps, your developers can spend more time developing applications than configuring or maintaining Kubernetes clusters.

    GitOps strategies are gaining traction with enterprises for various reasons, including improved productivity, increased deployment frequency and improved security. While it’s true that GitOps continues to be a transformative trend, the increased speed and automation can also lead to misconfigurations that compromise application stability and security.

    Figure 2: CI/CD using GitOps

    Often, these security vulnerabilities delay development as developers spend significant time and effort investigating them. Since this poses a hindrance to progress, the question that arises is how enterprises can maintain the frequency and speed of deployment while remaining secure and reliable. This is where trusted delivery comes in.

    An Extension of DevOps

    DevOps is a software engineering practice and culture that blends development and operations to provide a shared and collaborative approach to developing and maintaining software. This can boost productivity, lower costs and increase customer value through increased collaboration.

    GitOps extends DevOps to bring the deployment workflow closer to developers. GitOps integrates code and configuration management for cloud-native application deployment and administration, while DevOps fosters shared ownership, transparency, and rapid feedback.

    GitOps uses DevOps-proven practices like version control, code review and CI/CD pipelines with any Git-based repository, including GitHub, GitLab or BitBucket. It builds on DevOps and applies DevOps best practices for infrastructure automation.

    What is Policy-as-Code?

    When deploying and managing cloud-native infrastructures, adhering to security best practices and regulatory standards is critical without hindering development. Policy-as-code is an approach to infrastructure security in which rules are codified and enforced, thereby reducing or eliminating human errors. You can use it to write applications using a high-level language to control, manage and automate policies. With policy-as-code in place, developers may create applications to manage security, compliance and other regulations seamlessly.

    Here are the types of policies that you can codify:

    • Security Policies: By adhering to these policies, you can protect your software from data breaches caused by misconfigurations.
    • Resilience Policies: The use of these policies in your Kubernetes clusters will help you create highly available, fault-tolerant systems as well as maintain business continuity.
    • Coding Standards Policies: These policies can enforce coding consistency, i.e., any code that violates these policies will be rejected automatically by the system.

    What is Trusted Application Delivery?

    Trusted delivery entails codifying security policies within your software delivery pipeline for security and compliance and introducing guardrails at every stage. By ensuring the security, integrity and reliability of your applications deployed in production environments, unauthorized access, tampering or malware introduction is minimized. It establishes and maintains trust in application source code, artifacts and deployment pipelines throughout the SDLC process.

    The key objectives of trusted application delivery are:

    • Security: It protects the application from security threats and vulnerabilities
    • Integrity: It verifies the authenticity and integrity of the source code of your application, together with the dependencies, and any artifacts
    • Reliability: It ensures that the applications deployed to the production are stable and are devoid of any security loopholes so that they can perform as desired

    Trusted application delivery helps development teams release applications early and secures them with automated protections or guardrails. It extends the GitOps pipeline by integrating security, governance and verification with policy-as-code. These security measures are implemented with policy-as-code. The policy-as-code approach encompasses the entire SDLC, including the design, development, testing and deployment stages.

    According to your organization’s security and compliance policies, compliance and security rules are codified and integrated into the system. Throughout the life cycle of your application, you can manage its security, compliance and other regulations using policy-as-code. These code-defined policies prohibit the provisioning of non-compliant resources. You should implement trusted application delivery practices to secure your organization’s application delivery process and reduce the likelihood of unauthorized access.

    Here are the key benefits of trusted delivery:

    • Faster and more frequent releases
    • Enhanced security
    • Protect against misconfigurations
    • Avoid compliance violations
    • Improved collaboration

    What is DevSecOps?

    DevSecOps is a software development methodology that blends development (Dev), security (Sec) and operations (Ops) at all phases of the SDLC by incorporating mandatory security checks. An organization of any size can benefit by creating a culture of shared responsibility and collaboration by bridging the gap between development, security and operations teams.

    A DevSecOps pipeline adds practices such as scanning, policy enforcement, static code analysis, threat intelligence and compliance validation to the SDLC process. Contrary to traditional software development processes that treat security as an afterthought leading to security vulnerabilities and breaches, DevSecOps is a paradigm shift that fosters a culture of proactive and continuous security approach to help address security concerns and vulnerabilities.

    DevSecOps and GitOps help organizations adapt quickly to changing emerging trends and requirements. DevSecOps is an offshoot of DevOps that focuses primarily on security throughout the development process. It helps detect and resolve security issues and vulnerabilities easily and quickly while fostering a culture of continuous improvement.

    Here are the benefits of DevSecOps at a glance:

    • At the outset, determine which points in the SDLC process are susceptible to security flaws and vulnerabilities in the SDLC process
    • Streamlined compliance
    • Secure supply chain
    • Reduced risk of the attack surface
    • Complete visibility of potential threats

    Why DevSecOps?

    DevSecOps is a proven way to secure your CI/CD pipelines, deliver secure, trusted, high-quality software and foster an environment where security is the responsibility of everyone in the organization. By shifting security left—i.e., introducing security into the CI/CD pipeline earlier—organizations can be much more productive and the teams can be more cooperative and collaborative.

    Given the threats to the security of the software supply chain, DevOps practices have given way to DevSecOps practices, where safety is paramount and a fundamental and ongoing component of software development. At the outset of the SDLC, you should ensure that software components and dependencies are secure to avoid potential software supply chain hazards.

    Additionally, it would help if you had built-in security gates in each step of the SDLC to ensure consistent, repeatable and automated processes from the software build phase to deployment. Remember, DevSecOps isn’t an option; instead, it is a necessity.

    Trusted Delivery With GitOps

    Automated software delivery pipelines are based on Git as the only source of truth. Policy-as-code is a great way to ensure that misconfigurations and errors are detected, alerted, and deployments halted automatically. Next, we’ll cover how a typical GitOps workflow determines where multiple checkpoints can be implemented for trusted delivery.

    In a typical workflow based on GitOps, a developer commits the source code to a Git repository. The CI tool then builds the container images. These container images are then tested and then uploaded to an image repository. At the end of this process, these container images will be deployed to a Kubernetes cluster. GitOps agents compare the current cluster state with the desired state on a continuous basis.

    Hence, you should implement security guardrails at the following points:

    • Git repository
    • CI stage
    • Configuration repository
    • Kubernetes cluster

    Creating a Trusted Software Supply Chain With DevSecOps

    You can build a trusted software supply chain by incorporating security practices throughout the SDLC to ensure the integrity and security of the software delivered by DevSecOps. Here is a step-by-step guide to building a trusted software supply chain by leveraging DevSecOps principles:

    • Security by Design: You should integrate security right from the outset of the SDLC and identify the key threats to the security of the pipeline.
    • Application Dependency Scanning: When building applications, we often use third-party dependencies to accelerate the development process. You should monitor security vulnerabilities and patches as they become available for these dependencies. You can leverage OWASP Dependency Check, Firewall from Sonatype, etc.
    • Secure Coding Practices: You should adhere to and enforce these practices among your development teams. You can integrate static code analysis tools into your DevSecOps pipeline, conduct regular code reviews and identify and fix security issues.
    • Container Security: You should scan container images regularly for security vulnerabilities, implement strategies to secure your containers and use container registries with access controls.
    • Continuous Compliance: You should ensure the software supply chain adheres to the relevant standards and guidelines for compliance requirements. In this regard, you should conduct audits regularly to check if compliance requirements have been satisfied.
    • Collaboration and Communication: You should be able to foster a culture in your organization that facilitates collaboration and communication across the development, operations and security teams.

    DevOps, DevSecOps and GitOps: The Future

    As organizations strive to optimize the efficiency of their software supply chain, DevOps, DevSecOps and GitOps are approaches that can improve collaboration, efficiency and security, to help software developers and IT operations succeed. These practices would become more critical and widely used in the future. GitOps provides teams with better tools for managing configurations so they can focus on more strategic activities.

    While GitOps is an approach that advocates Git-based version control systems as the single source of truth, DevOps is concerned about a cross-functional team that can work together. DevSecOps goes a step further by blending security into all stages of the SDLC process.

    The future of DevOps, DevSecOps and GitOps will see increased automation, tighter security integration and a sustained emphasis on efficiency and collaboration. Your organization should adopt these approaches to provide reliable software and services to its customers.

    Conclusion

    Trusted application delivery is essential for securing applications, protecting user data, meeting compliance requirements and preserving market credibility and trust. It helps enterprises and end consumers by ensuring that software is distributed securely and reliably. Organizations can ship high-quality applications with guaranteed security and performance by adopting trusted delivery practices.

  • Palo Alto Networks Extends CNAPP to CI/CD Platforms

    Palo Alto Networks Extends CNAPP to CI/CD Platforms

    Palo Alto Networks has extended the reach of its cloud-native application protection platform (CNAPP) to include the ability to secure continuous integration/continuous delivery (CI/CD) platforms.

    Daniel Krivelevich, CTO for application security for Prisma Cloud at Palo Alto Networks, said this extension of the Prisma Cloud platform would make it easier to better secure software supply chains as software builds are continuously updated by application development teams.

    Previously, Palo Alto Networks provided the ability to secure runtimes and secrets and scan code for vulnerabilities as it is being developed. This latest extension now adds the ability to secure the CI/CD platforms that drive DevOps workflows, he noted.

    While more organizations than ever are shifting application security responsibility further left toward developers, many of them overlook the need to secure the CI/CD platform itself. Most developers are not cybersecurity experts, so the chances they will miss something that can find its way into a software build are still high. Extending the reach of Prisma Cloud to the CI/CD platform will ensure the build that forms the foundation of the code is free of vulnerabilities when it finds its way into a production environment.

    That’s critical, because cybercriminals are becoming more adept at injecting malware into software supply chains in the hopes of being able to eventually compromise multiple downstream applications. In fact, this issue has prompted governments around the world to hold more organizations legally responsible for the security of the applications they build and deploy.

    There are, of course, multiple approaches to securing software supply chains, but Palo Alto Networks is making a case for an integrated platform that provides a comprehensive approach to securing the entire IT environment. Rather than integrating multiple point products, a CNAPP ultimately reduces the total cost of cybersecurity on an end-to-end basis, noted Krivelevich.

    It’s not clear how quickly organizations are transitioning to CNAPPs, but as that transition occurs, the overall state of application security should improve. One of the issues that has historically plagued application security is no one in an organization has been specifically tasked with ensuring it. Application development teams assumed cybersecurity professionals were allocating funds to it. Cybersecurity teams, however, tended to focus on areas they directly controlled, such as network firewalls. A CNAPP makes it simpler to deploy a cybersecurity framework in a more extensible way.

    Of course, there’s still much work to be done when it comes to prioritizing vulnerability remediation efforts. The truth is developers only have so much time to allocate to developing patches, so the focus needs to be on identifying the vulnerabilities that are the most severe in terms of the amount of damage that might be inflicted. At the very least, however, a CNAPP should provide the means by which the context required to identify those vulnerabilities could be more easily shared.

    One way or another, however, the number of vulnerabilities finding their way into production environments should steadily decline as more focus is put on securing software supply chains.

  • Automation or Concatenation?

    Automation or Concatenation?

    We’ve been in a period of mergers and acquisitions for a while in the DevOps and DevSecOps spaces, and it seems to be heating up even more. Combined with products expanding to cover a more broad segment of the software development life cycle (SDLC) and strategic partnerships to cover more of the SDLC, it is starting to look like our automation efforts are going to be increasingly driven by vendors. Those efforts will be more concatenation than automation–though technically the same in perception, they vary wildly in implementation, and we should be planning for it.

    Build Versus Buy

    We have always struggled with build versus buy, and we have always struggled with best-of-breed versus overarching solution set. The entire hype of Every. Single. Vendor. Claiming to be a platform is partially driven by the latter argument. Putting “platform” in your marketing does not make a vendor better at those things outside its historical strengths, though, so that movement is doomed to fail for most organizations. Some were already moving to be a platform and had assembled–by M&A or by internal development–an impressive platform already, and those were successful because they were good at a breadth of things. The best of them are continuing this trend. I try to avoid plugging vendors here, but I think an example is in order in this case.

    Akamai’s recent purchase of NeoSec is the poster child for vendors doing the platform thing super well but not resting on their laurels. I’ve followed Akamai’s security offerings for years, and the platform has impressive functionality, but NeoSec is exactly what they needed. It plugs a couple of holes in their security offerings and brings a whole customer base into the Akamai fold. That’s what I’m talking about when I refer to the good ones.

    The problem is the acquisition and expansion of offerings trend is starting to gain a lot of steam, and not every vendor jumping on the bandwagon does the job as well as Akamai does.

    This leaves enterprise IT staff responsible for DevOps and DevSecOps – even straight security staff – with a new set of issues. Best-of-breed is taking a hit again, and overarching solutions are on the rise. The difference is the breadth that some of these new “platforms” offer is massive. And we know that even the good ones will have better and worse parts of the overall solution. So IT now has to start considering what vendors call a feature and how it individually compares to the competition. This will require more up-front work for evaluators. Now, a larger (and growing) list of “features” that were once “products” has to be considered piece-by-piece against the competition, and organizations must evaluate the feasibility (and cost) of implementing two (or even multiple) massive platforms to get the most critical functionality needed.

    As of this writing, point solutions that do one thing extremely well are holding up, but the trend is obvious. Those markets will be subsumed into larger offerings that may not do as well at other parts of the overall problem. Evaluators will have to be more careful and consider their options with an eye to the larger infrastructure and problem domain. Do you want a single vendor doing all of the steps of DevOps or responsible for every aspect of application security? That will be an individual decision and, if not, how the final solution/architecture looks will be a more complex discussion.

    Integration and Automation

    In our quest for integration and automation, many vendors are responding with the concatenation of tools to solve the automation part. You’ll be stuck figuring out if they’re doing it well enough for the org.

    But that’s not a problem you can’t resolve. You’ve been rocking it through staffing issues, lockdowns, growth and shrinking… Just keep rocking it. Choose the solution that makes the overall best sense for your organization, get it running and take a break; head to the beach for some relaxation time. Or whatever you do to clear your head – because the next problem will still be there when you get back, and it’s better to be relaxed when you tackle it.