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Tag: Digital.ai

Digital.ai Extends Scope and Reach of DevSecOps Platform

An Erawan release of the DevSecOps platform from Digtal.ai adds an ability to automated applications security along with integration with Backstage, an open-source internal developer platform originally developed by Spotify that is now being advanced under the auspices of the Cloud Native Computing Foundation (CNCF).

In addition, Digital.ai is tightening its integration with the Microsoft Azure cloud platform by making it simpler to validate and manage Helm charts.

Digital.ai CEO Derek Holt said as the pace of application development starts to further accelerate, thanks to the rise of generative artificial intelligence (AI) tools, organizations will need to revisit their DevSecOps workflows. Specifically, they will need a platform with the governance capabilities required, to ensure that only code that has been thoroughly reviewed and tested makes it into a production environment, said Holt.

The company is also working toward adding generative AI tools to its DevSecOps platform to make it simpler to manage code bases that are about to rapidly expand, he noted. Digital.ai has been laying the foundation for that effort via a Digital.ai Intelligence data lake that already applies predictive machine learning algorithms to data collected by both Digital.ai and third-party partners, said Holt.

Over time, many of the capabilities of individual tools that DevSecOps teams once needed to integrate are becoming features of a larger platform to enable DevSecOps teams to better embrace platform engineering as a methodology for managing DevSecOps workflows at scale, he noted.

There are no shortage of options when it comes to DevSecOps platforms, but the ability to apply software engineering intelligence across normalized data collected from increasingly hybrid application development environments will prove crucial, added Holt.

Best DevOps Practices

It’s not clear to what degree organizations that have already invested in a DevOps platform might be willing to switch platforms in the age of AI-assisted application development. However, the number of organizations that will be able to create and deploy custom applications is about to substantially increase. Many of those organizations will eventually need to adopt a set of best DevSecOps practices at a time when governments around the world are moving to hold organizations that build and deploy software more accountable for application security.

In the meantime, DevSecOps teams should assume the amount of code simultaneously moving across pipelines will increase as developers take advantage of various generative AI tools to help them write. The challenge is the first generation of these tools has been trained using code samples of varying quality collected from across the internet. As such, the code generated by these tools might have known vulnerabilities or might not in some cases work at all. Each DevSecOps team, as always, will need to verify that code — regardless of whether it was created by a human or a machine — can be used in a production environment.

After all, the benefits of rapidly building applications are generally erased if the end-user experience deteriorates to the point where DevSecOps teams spend more time troubleshooting existing applications than deploying new ones.

June 17, 2024
Survey Surfaces Raft of Ongoing Agile Development Challenges

A survey of 788 software development professionals finds 71% are using agile development methodologies within their software development life cycle (SDLC) but only 44% said it is working very well (11%) or somewhat well (33%) in the enterprise.

Conducted by Digital.ai, the survey identifies end-to-end visibility and traceability from business initiative through deployment as the top opportunity for improvement (44%), followed by an ability to measure cycle time, wait time and bottlenecks (34%), continuous testing (29%) and identification and measurement of technological risk (28%).

Nearly half (46%) identified too many systems as a primary reason Agile was not scaling, followed by siloed teams (37%), culture clash (34%), inconsistent adoption (30%) and the inability to measure business value (28%).

Despite these issues, nearly two-thirds (63%) said they have visibility into their Agile pipeline, with 55% claiming they have complete visibility. However, only 36% said DevOps teams currently work together as one team to accelerate innovation and deployment of high-quality and more reliable software. At the same time, only two-thirds (66%) claimed at least 50% of their applications were delivered on time and “with quality.”

Joyce Tompsett, general manager of the State of Agile project at Digital.ai, said the survey makes it clear many organizations are still trying to strike the right balance when it comes to developing software. For example, the survey finds the top two reasons organizations adopt Agile (tied at 41%) are to accelerate time to market and prioritize delivery and measure customer/business value. Most software development teams are being evaluated both based on velocity (33%) and the value delivered (29%), the survey finds.

The survey also identifies customer satisfaction (43%), time to delivery (39%) and competitive advantage (34%) as their top priorities for 2024.

In general, the survey finds that 33% of respondents reported business leaders and executives are actively leading and participating in company-wide agile transformation. However, more (41%) said there was not enough leadership participation. Well over a third (37%) said business teams simply don’t understand what Agile is or what it can do. More than a quarter (27%) said there is not enough training.

Not surprisingly, the most widely adopted methodology is Scrum (63%) with the Scaled Agile Framework (SAFe) being the most widely used (26%). Another 22% don’t have a mandated framework, while 12% said they have created their own.

The debate over what precisely constitutes agile development is, of course, ongoing. Each organization tends to adopt the elements of the framework that make the most sense for them, so the actual state of adoption from one organization to the next will vary widely. The one thing that is certain is the more complex the IT environment, the more challenging it can be to embrace agile development.

And there continues to be lots of room for improvement. If only two-thirds of organizations are delivering half (50%) of their applications on time with an acceptable level of quality, it means there are more projects that are failing to meet that standard than many software development teams might care to admit.

As more organizations become more dependent on software, however, tolerance for that level of failure will only continue to decline as businesses and leaders lose patience with application development practices that do not live up to expectations.

February 7, 2024
Digital.ai Update Extends Scope and Reach of DevSecOps Platform

Digital.ai this week made generally available a Denali update to its DevSecOps platform that promises to make it simpler to integrate custom artificial intelligence (AI) models with the AI models developed by the company.

At the same time, the company is adding self-guided workflows and templates to generate tests and implement DevSecOps best practices along with integrations with Terraform by Hashicorp, Azure Bicep, Azure Key Vault and AWS Secrets Manager.

Finally, Digital.ai is adding an ARM Protection feature to better secure iOS applications without requiring embedded bitcode or integrations into the build system. DevOps teams can, via a single command, protect compiled applications locally with support for obfuscation, run-time active protections and application monitoring without uploading them to a third-party service.

Greg Ellis, general manager for application security for Digital.ai, said the overall goal is to make it simpler for software engineering teams to invoke capabilities that have been embedded within the company’s DevSecOps platform.

In the case of AI models, that also means instead of requiring DevOps teams to only use AI models developed by Digital.ai, the company is moving to make it simpler for DevOps teams that adopt its platform to incorporate custom AI models as they see fit as part of an ongoing effort to democratize intelligence at scale, he noted.

In general, it’s already apparent organizations will be employing heterogeneous approaches to incorporating AI models into DevOps workflows, said Ellis. The challenge now is moving beyond experimenting with AI to embedding them within DevOps workflows, he added.

It’s already clear developers are using generative AI to develop code at increasingly faster rates. The challenge now is to manage that accelerated pace of development when many organizations are already struggling to manage existing DevOps workflows at scale. Hopefully, AI technologies will also one day help software engineers find ways to manage that volume of code moving across their DevOps pipelines.

In the meantime, organizations will also need to better define where the machine learning operations (MLOps) workflows that data scientists use to build AI models end and where DevOps workflows that will be used to embed AI models into applications begin. As is often the case when it comes to emerging technologies, cultural issues are just as challenging as the implementation hurdles that need to be overcome.

At this point, like it or not, the generative AI genie is out of the proverbial bottle. Just about every job function imaginable will be impacted to varying degrees. In the case of DevOps teams, the ultimate impact should involve less drudgery as many of the manual tasks that conspire to make managing DevOps workflows tedious are eliminated. Less clear is to what degree AI may drive organizations that have already embraced DevOps to adopt an alternative platform, but savvy DevOps teams are, at the very least, starting to map out which processes are about to be automated so they can have more time to focus on issues that add more value to the business.

October 27, 2023
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.

August 2, 2023
Digital.ai Extends Scope and Reach of DevOps Platform

Digital.ai today unveiled the Ascension release of its namesake DevOps platform that adds pre-built metrics and best practices dashboards using a Test Lens capability to more easily employ analytics to identify software delivery bottlenecks.

In addition, the company has added autonomous testing tools that automatically generate performance and security tests for mobile applications. These tools can be run by DevOps teams, developers or business users. An enhanced Change Risk Prediction capability enables machine learning algorithms to make more extensive use of historical data.

Finally, Digital.ai has also added integrations with the open source Argo continuous integration/continuous delivery (CI/CD) platform and made available an Agility Sync tool for building custom integration with legacy application environments and opened an online marketplace for integrations.

Jeff Moloughney, chief marketing officer for Digital.ai, said the goal is to extend the reach of the company’s DevOps platform. At its core, the platform has a value stream management (VSM) and version control software originally developed by XebiaLabs and CollabNet VersionOne before the two companies merged to create Digital.ai. The combined company subsequently acquired Numerify, a provider of analytics tools infused with artificial intelligence (AI), Experitest, a set of testing tools, and Arxan, a provider of an application security platform. The company claims that its platforms are used by more than 1,500 organizations.

In general, DevOps is entering a new era as organizations realize how dependent they are on software to drive business processes, noted Moloughney. Historically, the adoption of DevOps practices has been driven from the bottom up within most organizations. Now, business and IT leaders are also looking to drive the adoption of a consistent set of DevOps best practices to gain more visibility into software delivery, he added.

That does not mean, however, that organizations can dictate what tools individual developers can use, so the DevOps platform employed has to be extensible in terms of the available integrations, said Moloughney.

At the same time, Molougney added that more DevOps processes will be automated using machine learning algorithms. Digital.ai is embedding these algorithms within its core platform to both uncover software delivery bottlenecks and automate routine tasks such as testing.

It’s not clear to what degree organizations that have already embraced DevOps might be considering replacing their existing platforms but, regardless of size, most of them are encountering issues. On the plus side, the number of organizations that are adopting DevOps practices more broadly is increasing as more digital business transformation initiatives are launched. As those practices evolve, the expectation is many of those organizations will embrace analytics in the form of VSM to better optimize their efforts.

In the meantime, the rate of DevOps adoption across enterprises will continue to vary widely. Each development team within an organization tends to define a set of DevOps practices based on their own requirements. The challenge, given all the dependencies that exist between those projects, is determining the actual rate of progress the organization is making as each of those isolated initiatives progresses.

April 27, 2022
Digital.ai Deepens Value Stream Management Integration

Digital.ai announced today it has expanded usage of machine learning algorithms within its namesake value stream management (VSM) platform to include modules to optimize flow and improve application quality in addition to enhancing its risk prediction and process optimization modules.

In addition, Digital.ai has expanded the number of pre-built integrations it now provides for hundreds of DevOps tools and platforms.

Derek Holt, general manager of agile and DevOps for Digital.ai, said the goal is to expand the bi-directional flow of data between the core Digital.ai platform and modules and tools provided by the company and its third-party DevOps partners.

Digital.ai was formed from the merger of XebiaLabs and CollabNet VersionOne. The combined company has rolled up Numerify, a provider of analytics tools infused with artificial intelligence (AI), Experitest, a set of testing tools and Arxan, a provider of an application security platform. With the latest release, these capabilities have been integrated into the core Digital.ai platform in a way that goes well beyond merely providing access via a common user interface, said Holt.

The goal is to provide organizations with a VSM platform through which better decisions can be made to optimize software development and delivery by first collecting data from all the DevOps tools employed and then using machine learning algorithms to surface optimization recommendations, noted Holt. As application development environments become more complex to manage, it’s already apparent that most IT teams will require AI capabilities embedded in a VSM platform to manage interdependent projects, noted Holt.

Armed with those recommendations, it becomes simpler to reallocate resources in the event a critical project is going to be completed later than planned or shift resources to make sure that project is completed on time. A VSM platform also enables IT organizations to assess the impact those decisions will have on the rest of the business at a time when organizations have never been more financially dependent on the timely delivery of software. The late delivery of a single application module can wind up having a profound impact on revenue projections for organizations that increasingly rely on digital processes to engage customers.

A recent survey conducted by Digital.ai found that many digital transformation initiatives are struggling, to some degree. Nearly half of respondents (49%) said they are not seeing the results they expected from their digital transformation efforts, and 54% admitted they were worried about their ability to compete. Over 90% need to see a better return on their digital transformation investments.

Digital.ai claims it currently has more than 1,200 customers, including Allianz, Commerzbank, eBay, Janus Henderson and Levi’s that are using elements of the Digital.ai platform to better manage software development projects. Many of the users of those tools now include business executives and other application stakeholders that are employing a VSM platform to track the progress of application development projects. Digital.ai Analytics Lenses that are domain-specific make it simpler for different classes of end users to access pre-built dashboards and metrics, noted Holt.

The market research firm Gartner has predicted that 70% of organizations will employ a value stream management platform by 2023 to improve flows across their DevOps pipelines. However, as DevOps platforms and project management applications continue to evolve, many are also adding many of the analytics capabilities found in a VSM platform. Each IT organization will need to decide which approach makes the most sense for them, but Digital.ai is clearly betting that as application development becomes more challenging, the need for an integrated VSM platform will become much more apparent.

May 26, 2021
Digital.ai: The Companies Formerly Known As XebiaLabs and CollabNet Plus

Another shoe has dropped in the continuing story of TPG Capital‘s foray into DevOps. The company formed by the merger of XebiaLabs and CollabNet VersionOne is now Digital.ai. Additionally, the company has acquired Arxan, a leader in application protection.

I had a chance to catch up with Digital.ai’s CEO Ashok Reddy and Arxan COO Mark Lorion, who will be Digital.ai’s COO of Platform going forward. The video of our discussion which is also available on TechStrong.TV, appears below.

What They Said

Reddy made it very clear that Digital.ai is not content with simply adding this AppSec company to the mix. It is actively involved in due diligence operations on several other acquisitions. Reddy and the TPG team feel that the current COVID-19 situation affords those with cash to pick up some bargains. That may have been the case with Arxan, which was owned by private equity firm TA Associates, which acquired Arxan in 2013 for $132 million. TA may have decided it was time to get off the merry-go-round, given the unsettled state of things right now.

In any event, TA’s loss is Digital.ai’s gain. Arxan adds some much-needed security muscle and a blue-chip bank customer list. But, of course, the real play here is building out the end-to-end DevOps company that will compete with GitLab, GitHub, CloudBees, JFrog and others. We detailed this market in an article when the original merger was announced.

My take is that indeed Digital.ai is not a complete company just yet. It may not even be done in the DevSecOps space, as Arxan is not your typical DevSecOps technology. It may have been more of an opportunistic acquisition.

Also, in terms of executive team, Reddy has been quietly assembling a team made up of some of the best people he worked with at previous stints at IBM, CA/Broadcom and the executives from the acquired companies.

Stay tuned for more on Digital.ai’s continued evolution in the months to come.

— Alan Shimel

April 15, 2020