Tag: ai

  • Automation Evolution: Is Your DevOps Ready for Tomorrow’s Innovations?

    Automation Evolution: Is Your DevOps Ready for Tomorrow’s Innovations?

    Let’s dive into a topic that’s constantly buzzing in our industry: DevOps pipelines. We’ve all experienced the incredible transformation DevOps brought, dismantling silos and accelerating software delivery. But here’s a critical question for 2025 and beyond: Is your pipeline evolving?

    This is critical to understand because, without you realizing it, your DevOps pipeline might be stuck in the paradigms of its early successes. In a world continually upping the game in agility and continuous innovation, even “traditional” DevOps workflows are starting to reveal strains. The underlying challenge? Often, it’s the need for a more profound, intelligent, and integrated approach to automation.

    Many organizations, despite their DevOps use, still wrestle with nagging inefficiencies. Think about siloed teams creating friction; manual processes, in critical stages like testing and deployment, inviting errors; or overly cautious approaches to release due to stated or unconscious fears of instability. These can lead to slower cycles than the business demands, and mounting pressure on development teams. Add in the common scenario of “tool sprawl,” a patchwork of disconnected tools that have naturally grown over time and that complicates workflows rather than simplifying them.

    Sound familiar? The truth is that the goalposts for software delivery haven’t just moved; they’re in constant motion. Digital transformation isn’t a project; it’s the new baseline. Businesses are compelled to harness cutting-edge technologies, with AI-driven automation becoming less of a futuristic vision and more of a present-day necessity to achieve speed, efficiency, and a sharper competitive edge. Security, too, can no longer be a checkpoint; it must be woven into the very fabric of the development lifecycle from the first line of code. To navigate this complex, high-stakes environment, traditional pipeline thinking—while revolutionary in its time—needs to evolve.  

    The Next Leap: Intelligent Automation for a Modern Pipeline

    So, how do we bridge the gap? The answer lies in a new generation of intelligent automation that’s holistic, adaptive, and deeply integrated. This isn’t merely about scripting. It’s about reimagining the entire software delivery value stream using a new source of power: smarter, more connected automation.

    Imagine a world where your development and operations teams aren’t just collaborating but are empowered by a unified platform that provides shared visibility, streamlines handoffs, and automates the mundane. This is where the true power of modern DevOps comes to life. By intelligently automating testing suites, build processes, and deployment strategies (think canary, blue-green), you not only accelerate delivery but also drastically reduce human error and mitigate release risks.  

    One of the biggest game-changers is the ability to gain end-to-end visibility and actionable insights. Modern automation platforms can offer real-time analytics across the entire delivery lifecycle. This means identifying bottlenecks before they become major roadblocks, optimizing resource allocation, and making data-driven decisions to continuously improve performance. When AI is layered on top, teams can even leverage predictive insights or query project states using natural language, making complex data accessible and actionable for everyone.  

    Finally, let’s get back to that dreaded tool sprawl. The right automation strategy doesn’t necessarily mean throwing out everything you have. Instead, it’s about integrating and orchestrating your existing toolchain—Jira, Jenkins, Git, Kubernetes, and more—into a cohesive ecosystem to create a single source of truth and control, simplify management, and get the most out of your current investments.  

    Automation as the Engine of Innovation

    The profound impact of evolving your DevOps pipeline with intelligent automation goes far beyond just “doing things faster.” When you liberate your talented teams from toolchain complexities and repetitive manual labor, you unlock more of their potential to innovate and create value.  

    This shift translates directly into compelling business outcomes:

    • Accelerated Innovation Cycles: Respond to market changes with greater speed, roll out new features more frequently, and consistently delight your customers.  
    • Optimized Resource Utilization: Reduce costly rework, minimize unplanned downtime, and empower your teams to focus on strategic initiatives that drive growth rather than just keeping the lights on.  
    • Enhanced Quality and Reduced Risk: Embed security and compliance throughout the lifecycle. Proactive governance and consistent, auditable processes significantly reduce the risk of production failures and ensure you meet regulatory demands.  
    • Improved Developer Experience: High-friction, piecemeal internal platforms can frustrate engineers. A well-architected automation approach provides a smoother, more productive experience, boosting satisfaction and retention. An advanced DevOps platform can even serve as the core foundation for organizations building their Internal Developer Platforms (IDPs), providing a robust “starter kit” that accelerates IDP initiatives while allowing for tailored expansion.  

    Are You Ready to Evolve?

    The DevOps landscape is in a perpetual state of change. The pipelines that served us well yesterday may become the bottlenecks of tomorrow if they don’t evolve. Embracing intelligent, AI-driven automation isn’t just a trend; it’s a strategic imperative for any organization looking to thrive in the Digital+ Economy. It’s about building a resilient, adaptive, and innovative software delivery capability that’s ready for whatever comes next.

    Perhaps it’s time for a candid look under the hood. The question isn’t just if DevOps pipelines need to evolve with automation, but how quickly and intelligently your organization will lead that charge. Consider this an invitation to critically evaluate your DevOps pipeline. Where are the bottlenecks? Are your automation strategies keeping pace with what’s possible? A thorough self-assessment can illuminate the path to unlocking new levels of productivity and innovation.

  • Survey: Pace of Increased Adoption of GitOps Varies Widely

    Survey: Pace of Increased Adoption of GitOps Varies Widely

    A survey of 660 software development professionals finds 93% have embraced GitOps as a methodology for building and deploying software, with 68% planning to increase usage.

    Conducted by Octopus Deploy, a provider of an automation platform for releasing software, the survey finds another quarter (25%) plan to retain their current usage compared to 7% that plan to reduce or stop GitOps usage.

    Steve Fenton, director of developer relations for Octopus Deploy, said the survey also makes it clear that GitOps maturity levels also tend to vary considerably. For example, under one-third of organizations use GitOps across less than 20% of their production systems. The survey also finds only 35% of respondents that have adopted GitOps make use of continuous reconciliation and automatic rollback mechanisms. That suggests that many organizations might only be going so far as to house infrastructure-as-code (IaC) configurations alongside source within a centralized Git repository.

    GitOps, at its core, refers to the process of automating the provisioning and management of IT infrastructure using a set of best practices that extend to include IaC tools. Ideally, a GitOps workflow should ensure a system is managed declaratively, automatically pulls the desired state declarations from the source, stores that data in a way that enforces immutability and versioning, and then provides some way to observe those interactions.

    Once infrastructure is managed with code, it becomes easier to converge the management of IT operations and application deployment. Kubernetes, meanwhile, provides an ideal GitOps platform because it presents a standard set of application programming interfaces (APIs) that enable clusters to automatically pull code as required versus requiring an IT team to manually push code out to a platform.

    Regardless of the level of GitOps maturity, there tends to be a high correlation between adoption of GitOps and modern IT platforms such as Kubernetes, noted Fenton. In general, many organizations are typically inclined to rethink legacy processes whenever a new platform is adopted, he added. In fact, it’s not uncommon for a team that has adopted GitOps to deploy microservices-based applications to be working along software engineering teams that are using legacy approaches to deploy more monolithic applications, said Fenton.

    A GitOps workflow should ultimately make it simpler for IT operations to achieve continuous delivery of applications, a goal that has remained elusive despite the widespread deployment of continuous integration/continuous delivery (CI/CD) platforms. In fact, while CI is widely used to build applications, the percentage of organizations consistently using CD to deploy software has remained comparatively small.

    It’s not clear to what degree the rise of platform engineering as a methodology for managing DevOps workflows at scale might drive increased adoption of GitOps. The one thing that is certain is that as the pace of application development continues to accelerate in the age of artificial intelligence (AI), the need to revisit software engineering workflows will become apparent. The only issue to be resolved now is determining how much to revamp existing workflows versus opting to simply start over again from scratch.

  • Kusari Adds AI Security Tool to Inspect Code as Pull Requests Are Made

    Kusari Adds AI Security Tool to Inspect Code as Pull Requests Are Made

    Kusari has added an artificial intelligence (AI) tool that runs a security risk assessment every time an application developer makes a pull request.

    Company CTO Mike Lieberman said Kusari Inspector is designed to make it simpler for security to be shifted left toward application developers in a way that doesn’t impinge productivity.

    Most application developers care about security, but getting the information they need to ensure it has historically been problematic, said Lieberman. Kusari Inspector makes use of AI and dependency graph analytics to surface issues long before vulnerabilities and other structural flaws are incorporated into a software build, he said.

    Using a tool priced at $10 per seat per month, Kusari Inspector continuously learns about the codebase and individual application developer preferences, making it possible to generate instant, context-rich, annotated security reports with inline explanations for every new or updated pull request. Developers are then provided go/no-go guidance, remediation suggestions, and step-by-step instructions to mitigate risks such as exposed credentials, misconfigurations, typosquatted or maliciously named dependencies and prohibited licenses.

    In addition to identifying those issues, Kusari Inspector will also rank risky, low-trust, or vulnerable dependencies—direct and transitive—based on multiple data sources to provide developers with a deeper understanding of application security issues, said Lieberman. Developers via a natural language interface can also chat with Kusari Inspector to clarify findings, customize recommendations, and set security standards.

    Finally, Kusari Inspector will automatically generate and collect source data for a software bill of materials (SBOM) across all connected projects and repositories.

    The core Kusari platform leverages AI to identify dependencies and understand the blast radius of incidents using metadata it collects from throughout the software supply chain. That capability is now being extended further left to provide application developers with a tool that enables them to write more secure code.

    It’s not clear how much DevOps teams are investing in AI, but a recent Futurum Group survey finds that 41% of DevOps professionals expect generative AI tools and platforms will be used to generate, review and test code. A separate Futurum survey of cybersecurity leaders finds all are investing in software supply chain security, with application security posture management (ASPM) and DevSecOps automation and orchestration topping the priority list, followed closely by security composition analysis (SCA) tools, application programming interface (API) security and dynamic application security testing (DAST) tools. In addition, 30% of respondents expect to be piloting a software bill of materials (SBOM) initiative in the next 24 months/

    However, the source of the funding for these initiatives is becoming more of a shared responsibility, with only 21% of respondents reporting that security budgets are the sole source. In fact, half of the respondents (50%) noted that application development teams now own responsibility for application security.

    Overall, only 25% of respondents said there is limited collaboration with application development teams, resulting in occasional friction, compared to 59% that said there is good collaboration with room for improvement. Only 16% said there is a tight partnership based on shared goals.

    Hopefully, AI tools will continue to close that gap in a way that reduces the current level of stress felt today by all concerned.

  • Fast Code, Real Risks: Guardrails for AI-Generated Software

    Fast Code, Real Risks: Guardrails for AI-Generated Software

    Yonatan Arbel, developer advocate in the Office of the CTO at JFrog, dives into the potential risks and rewards of relying on artificial intelligence (AI) agents to build and deploy software.

    Yes, more code is shipping faster than ever, Arbel admits, but volume alone isn’t a win—each hastily synthesized line must still survive build servers, security scanners and production reality. “Not everything in life comes for free,” he reminds, framing AI acceleration as an IOU that must eventually be paid down with careful review.

    To drive the point home, Arbel shares a personal misadventure. While traveling, he used a “vibe-coding” platform to whip together a receipt-tracking app. The tool auto-generated an impressive login flow and slick UI, so he proudly shared the URL with friends—only to discover their receipts mixed with his the next morning. A quick code dive revealed an AI-written SQL query that fetched every user’s data instead of scoping to the current account, proving how easily privacy can be breached when humans skip the boring inspection step.

    From there, the conversation turns to guardrails. Developers are becoming “pilots with copilots,” Arbel notes, spending more time instructing chat windows than writing functions. That shift demands automated policy checks, dependency vetting and runtime governance so velocity doesn’t eclipse trustworthiness. DevOps teams must embed quality gates early and often, treating AI-generated packages and prompts just like any other third-party code subject to organizational rules.

    Arbel’s advice is pragmatic: Start small, review everything, and remember that speed without oversight is just a fast track to technical—and financial—pain. Used thoughtfully, AI can compress months of work into days, but only if teams stay vigilant, enforce context-aware policies and let security evolve in lockstep with the bots.