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Category: AI

  • PagerDuty Applies AIOps to IT Incident Management

    PagerDuty Applies AIOps to IT Incident Management

    PagerDuty, at its online Summit20 conference today, unveiled a bevy of updates that apply machine learning algorithms across a wide range of workflows across the PagerDuty incident response platform.

    In addition, PagerDuty today announced it will acquire Rundeck, a provider of an IT automation platform that auto-remediates incidents by enabling playbooks to automatically execute whenever there is an issue, for $100 million.

    Jonathan Rende, senior vice president of products for PagerDuty, said the overall goal is to make IT teams more efficient by applying AIOps at a time when organizations are trying to reduce to the total cost of managing increasingly complex IT environments.

    At the same time, Rende said PagerDuty is also moving to reduce the fatigue that inevitably ensues when trying to determine the relationship between the thousands of alerts that all the elements of an IT environment can now generate. The goal is to provide IT teams with more context and actionable intelligence, said Rende.

    As part of that effort, PagerDuty is now integrating change events that occur in code repositories via new integrations with GitHub, Puppet and Evolven. The bulk of IT incidents stem from changes made to the IT environment so via these integrations it will become easier to identify the most likely root cause of an issue, said Rende.

    PagerDuty has also added new capabilities to its Service Directory that employs machine learning algorithms to dynamically surface dependencies between people, changes, incidents and services in real-time. As IT environments become more complex thanks to the rise of microservices, he said it’s now more critical than ever to track relationships across workflows spanning multiple IT teams. The challenge is that existing manual processes can keep pace with the rate of change now occurring in IT environments, Rende added.

    PagerDuty is also moving to reduce the number of alerts that are generated by enabling the PagerDuty Event Intelligence module to surface recommendations generated by machine learning algorithms that suggest ways to group alerts to better streamline incident responses. In addition, IT teams can now pause alerts if they decide to allow systems to auto-remediate before they decide to intervene.

    The company is also extending the reach of an application programming interface (API) for analytics it created to enable IT teams to launch queries against the data collected by PagerDuty. In addition, PagerDuty is making available an Analytics Maturity Model that provides access to benchmark data from more than 13,300 customers to both compare their efforts with other IT teams and discover additional best practices.

    PagerDuty today also announced one-click integration with the Zoom video platform to enhance collaboration, as well as extending its existing integration with Microsoft Teams to include a similar one-click capability.

    Finally, PagerDuty also launched an instance of its platform for customer service teams that also provides enhanced integrations with Zendesk and Salesforce Service Cloud. With more organizations dependent on digital business processes to survive, Rende said the need to automate customer service processes has never been more pressing.

    At the same time, IT teams are struggling to keep pace with the rate of tickets being generated by employees who are now working from home. Put it all together and it becomes abundantly clear that continuing to rely on legacy approaches to IT incident management will simply not suffice.

  • Puppet Deepens Windows Integration

    Puppet Deepens Windows Integration

    Puppet today announced it has extended support for Windows environments across its portfolio of IT automation tools by adding a Group Policy Migration capability that enables multiple Windows configurations to be tracked simultaneously and offering to assist organizations seeking to deploy the open source Chocolately package manager.

    In addition, Puppet has extended its support for Microsoft tools by adding a Puppet PowerShell Desired State Configuration (DSC) Builder module, through which the Powershell tools Microsoft relies on to configure operating systems are directly exposed.

    Finally, Puppet has also added a Puppet VSCode Extension that gives Windows administrators the ability to invoke the Puppet Domain Specific Language (DSL) to access capabilities such as Intellisense and error-checking using their existing user interface and text editor for Windows.

    Puppet CTO Abby Kearns said interest in IT automation has significantly increased in the wake of the downturn brought on by the COVID-19 pandemic. While IT organizations that have been running instances of Linux have had access to a broad range of IT automation tools for years, Windows environments have not enjoyed the same level of parity even though half the workloads organizations are trying to manage today are based on Microsoft.net code, she said.

    With more pressure to automate than ever, Puppet sees an opportunity to fill that void, Kearns added.

    By relying on Puppet tools to automate both Linux and Windows platforms, an opportunity to reduce the total cost of IT also presents itself as organizations look to either limit or reduce IT headcount. Puppet claims there are already more than 40,000 organizations using its tools, including more than 80% of Global 5,000 organizations.

    It’s not clear to what degree Microsoft is invested in IT automation tools, especially compared to IT vendors that make managing infrastructure as code both inside and out of the cloud their sole focus. However, since it launched Azure Microsoft has been investing in tools to manage infrastructure as code. The depth of the Puppet portfolio enables IT organizations to also apply IT automation to operating systems and now to applications via Chocolately.

    Less clear is to what degree IT organizations may finally unify the management of Windows and Linux systems as part of an effort to reduce costs in the wake of the downturn. Most organizations continue to manage Windows and Linux systems in isolation. However, as IT organizations embrace IT automation more broadly, many are looking to standardize on a common set of best DevOps practices whenever possible.

    In the meantime, the opportunity to extend IT automation into the midmarket remains significant. Most enterprise IT organizations have already embraced IT automation to a significant degree. Midmarket IT teams have generally not had the skills and resources required to manage infrastructure as code. However, as IT automation tools become more accessible to the average IT administrator, there may come a day soon when broad swaths of IT systems deployed by midmarket IT organizations will finally be automated.

  • NVIDIA to Create AI Powerhouse by Acquiring Arm for $40B

    NVIDIA to Create AI Powerhouse by Acquiring Arm for $40B

    NVIDIA has announced its intention to acquire Arm from SoftBank for $40 billion as part of an effort to create a juggernaut that will build processors optimized for artificial intelligence (AI) applications that span edge computing deployment to the cloud.

    Under the terms of the proposed deal, Arm will operate as a subsidiary of NVIDIA and will continue to headquartered in Cambridge, UK. NVIDIA will pay to SoftBank a total of $21.5 billion in NVIDIA common stock and $12 billion in cash, which includes $2 billion payable at signing. SoftBank will retain a 10% stake in NVIDIA.

    NVIDIA CEO Jensen Huang during a conference call also committed to licensing the software NVIDIA uses to make graphical processor units (GPUs) using the same licensing model Arm has employed to encourage manufacturing partners to create processors based on its designs. In theory, that approach could substantially lower the cost of GPUs once the deal closes.

    However, because of all the regulatory approvals required, Huang said NVIDIA could not commit to a timeframe for the close of the deal. Previously, Huang noted it took NVIDIA a year to close the acquisition of Mellanox, a provider of storage and networking infrastructure.

    In addition to providing processors that drive gaming and CAD/CAM applications, GPUs from NVIDIA are widely employed to train AI models more efficiently than x86 processors from Intel or AMD. Processors from Intel are used more often to run the inference engines for an AI model once it is deployed. With the acquisition of ARM, NVIDIA gains access to processor technology that is emerging as a rival architecture for deploying AI models on a wide range of server and mobile computing platforms. Currently, Arm is best known for providing processors for mobile computing devices such as smartphones, which eventually will also be infused with a wide variety of machine and deep learning algorithms.

    In the meantime, many organizations continue to struggle with implementing AI. It typically takes a team of data scientists six months or more to build an AI model. Once that model is built, it then needs to be incorporated into an application as part of a DevOps workflow. In the meantime, business assumptions may change, rendering the AI model irrelevant. That requires the data science team to build a new AI model. In addition, as new data sources become available, AI models need to be updated. Most organizations are just now defining the processes for building and deploying those models, otherwise known as machine learning operations (MLOps), using many of the principles that were first defined by DevOps teams.

    Once those MLOps processes are defined, of course, IT teams then need to meld MLOps and DevOps workflows to keep applications that are updated more frequently synchronized with the AI model on which those applications increasingly depend.

    At this point, it’s clear every application will incorporate some level of AI. However, the speed at which AI might become pervasive is not likely to be all that fast anytime soon.

  • Algorithmia Looks to Meld MLOps and DevOps

    Algorithmia Looks to Meld MLOps and DevOps

    Algorithmia, in the latest update to the enterprise edition of its namesake machine learning operations (MLOps) platform, is enabling software development lifecycle practices to be applied to the building of algorithms by making it possible to debug them using desktop tools that are widely employed.

    The enterprise edition of Algorithmia will enable users to write and run local tests for algorithms as shared local data files. Desktop developer tools that can now integrate with that process include PyCharm, Jupyter Notebooks, R Shiny, Android, iOS, Cloudinary, Datarobot and H2O.AI.

    In addition, Algorithmia MLOps has extended its support for the latest graphical processor units (GPUs) available on Amazon Web Services (AWS) and Microsoft Azure.

    Finally, the company has added support for AWS C2S, a private cloud for intelligence services, and AWS GovCloud.

    Algorithmia CEO Diego Oppenheimer said IT teams building artificial intelligence (AI) applications are applying many of the same best DevOps practices employed elsewhere in the organization. Making it possible to debug those applications on a local desktop makes it easier for organizations to construct those DevOps workflows, he said.

    The Algorithmia platform enables IT teams to control the provenance of all components of ML operations, including certificate authorities, operating systems, container images, code, dependencies and ML models employed.

    Many organizations today are struggling with how to meld MLOps and DevOps processes. The rate at which AI models are created and updated is typically much slower than the rate at which the applications those models are infused is typically much slower. Data scientists are now coming to terms with how to deploy and update AI models within the context of a larger set of DevOps processes.

    In the wake of the COVID-19 pandemic, interest is AI is rising. Organizations are looking for ways to leverage machine learning algorithms to both drive down costs and increase revenue by automating a wide range of tasks. A recent global survey of 2,737 information technology and line-of-business executives conducted by Deloitte finds nearly two-thirds of the survey respondents (61%) said AI will substantially transform their industry in the next three years. More than half (53%) also noted they have spent more than $20 million during the past year on AI technologies and the talent required to master them.

    However, the same survey noted over half the respondents (56%) said their organization is slowing adoption of AI technologies because of the emerging risks. Obviously, improving the debugging process for applications that incorporate machine learning algorithms could go a long way toward reducing those risks.

    It’s not clear to what degree AI might one day transform the application experience. It is safe to say that eventually, almost every application will incorporate machine and deep learning algorithms to automate one or more processes. Those algorithms won’t eliminate the need for humans as much as they will automate rote processes that today consume more time than anyone prefers. The challenge, of course, will be making sure algorithms perform those tasks consistently as more data becomes available.

  • OpsRamp Adds Synthetic Monitoring to AIOps Platform

    OpsRamp has extended the capabilities of its namesake AIOps platform to include synthetic monitoring capabilities alongside existing tools for monitoring end user experiences.

    Bhanu Singh, senior vice president of engineering and DevOps for OpsRamp, said the Summer 2020 Release of the software-as-a-service (SaaS) platform adds synthetic monitoring that not only identifies bottlenecks across multi-step transactions but also correlates transaction performance such as page responsiveness and page timeouts with business metrics such as conversion rates and completion rates.

    By combining synthetic and end user monitoring capabilities, OpsRamp is effectively reducing the total cost of monitoring at a time when many organizations are under increased pressure to reduce costs, noted Singh.

    The OpsRamp Summer 2020 Release also improves monitoring with transaction latency breakdown for each step that contributes to user experience issues.

    It also adds visualizations for event correlations enabled by machine learning algorithms, support for HTTP response header logging for web performance insights and the ability to customizable transaction steps to provide more context.

    The OpsRamp Summer 2020 Release also adds 22 monitoring integrations for Amazon Web Services (AWS), including AppStream, Cognito, EventBridge, GuardDuty, Lex, OpsWorks and SageMaker.

    OpsRamp has added alert integrations with AppDynamics, Microsoft Systems Center Operations Manager, Sumo Logic and Sysdig. In addition, OpsRamp can now consume Sysdig events.

    Finally, OpsRamp is making it possible to access alert sequencing explainers that describe how the machine learning algorithms work. Alert sequence policy status shows model training progress at each stage of the event management life cycle.

    Singh said this latter capability is critical if IT teams are to gain confidence in AIOps platforms such as OpsRamp. In the wake of the downturn brought on by the COVID-19 pandemic, the need for AIOps has never been more pronounced. IT organizations are now under more pressure than ever to automate IT operations as part of a larger effort to reduce costs.

    The challenge they face is IT environments have arguably never been more complex. In many IT environments, the level of complexity is no longer feasible to manage IT processes manually. Automating IT processes is challenging because in most cases the IT environments have been customized extensively. AIOps presents an opportunity to employ machine learning algorithms to learn how each application environment functions and then automate it based on best practices.

    At this juncture, it’s more of a question of the degree to which IT organizations will be relying on AIOps. It may be a while before IT teams gain confidence in AIOps tools. However, with each passing day, machine learning algorithms become a little bit smarter. They may never have the intelligence of a human IT administrator; however, they never forget what they learn, require time off or quit to take another job.

    On the downside, of course, is that should machine learning algorithms make a mistake, the potential damage to an IT environment could be significant given the level of scale machine learning algorithms operate on. As such, IT organizations will always be well-advised to make sure the appropriate level of human supervision is always available.

  • Micro Focus Extends AI Testing Tools Reach

    Micro Focus Extends AI Testing Tools Reach

    Micro Focus has extended the capabilities of a family of Unified Functional Testing (UFT) testing tools infused with artificial intelligence (AI) to include support for both web-based and mobile applications.

    In addition, UFT testing tools will now allow a single script to automatically run on multiple platforms and browsers, eliminating the need to create multiple scripts for each platform.

    A crowdsourced feedback mechanism to improve recognition of objects such as a new widget has also been added to improve the AI model embedded in Micro Focus testing tools ability to recognize unique user interface paradigms.

    Finally, Micro Focus is making available an AI software development kit (SDK) and natural language processing (NLP) engine that makes it possible for both technical and non-technical users to create tests.

    Archie Roboostoff, product director for Micro Focus, said the natural language capability will make it possible to include end users of an application more easily in the functional testing process.

    As the responsibility for testing continues to shift left, he said, it’s become more important to automate as much of the functional testing process as possible. Developers and end users typically don’t have the time or often the expertise needed to build these tests.

    The challenge organizations face is the more application code they build and deploy, the more time they often spend on debugging applications. Many organizations wind up diverting resources from application development projects just to keep pace with the rate at which applications are now being built and deployed, noted Roboostoff.

    The paradox is that diversion of resources conspires to constrain the rate at which any additional applications are built. AI can play a critical role in breaking that logjam by reducing the time need to surface issues faster, said Roboostoff.

    At the same time, Micro Focus UFT tools further streamline the testing process by applying the AI capabilities it developed for mobile applications to web-based applications, he added.

    It’s not likely AI will eliminate the need for humans to test applications anytime soon. However, there are many routine issues that regularly manifest across different application development projects. As developers test more of their own code before it is officially promoted within the context of a DevOps process, it should be possible for development teams to rely on AI tools to surface issues they can address long before anyone knows they even existed.

    As is the case with every AI tool, an IT organization will need to gain confidence in the AI model embedded within any testing tool. The more AI tools are exposed to code, the more accurate they become.

    Regardless of the level of comfort with AI, it’s apparent application testing needs to become more automated. In the wake of the economic downturn brought on by the COVID-19 pandemic, most organizations will not be in a position to throw more bodies at application testing. The issue, of course, is most organizations have never been more dependent on applications to drive revenue at a time when most individuals are working and shopping from home more than ever to help contain the spread of COVID-19.

    The difference between success and failure during this so-called “new normal” will often come down to the level of testing applied to the applications on which many businesses are now almost solely dependent.

  • VMware’s Blue Medora Buy Advances AIOps

    VMware’s Blue Medora Buy Advances AIOps

    VMware has announced it will acquire Blue Medora, a provider of a service for integrating data collected by IT monitoring tools, to expand its efforts to advance the adoption of artificial intelligence to automate IT operations (AIOps).

    Roy Ritthaler, vice president of cloud management for VMware, said Blue Medora provides VMware with a software-as-a-service (SaaS) platform that is employed to inform VMware vRealize, an IT automation suite that is now being used by more than 400 customers.

    The data Blue Medora collects will also be employed within Project Magna, a research and development initiative that applies machine learning algorithms and other forms of artificial intelligence (AI) to automate IT operations, he said. Project Magna is based on a reinforcement learning engine that automates the tuning of multiple sub-components of the overall IT environment, resulting in what VMware calls “self-driving data centers.”

    Ritthaler said the goal is not to replace IT professionals as much as it is to eliminate rote tasks while making it easier to manage increasingly complex IT environments at scale. It then will become feasible for a smaller number of IT professionals to manage hybrid cloud computing environments, he noted.

    Blue Medora’s True Visibility Suite provides VMware with visibility not just into IT infrastructure but also all the way up the application stack.

    Ritthaler said it’s only a matter of time before most management frameworks are accessed as service, given all the data that needs to be collected and then analyzed to surface meaningful insights. It’s not clear how roles with IT organizations will evolve once those insights are surfaced regularly. Most enterprise IT organizations today rely on multiple specialists to optimize all the components that make up the IT environment. In theory, AIOps will make it possible for IT generalists to manage an IT environment without having to rely on so many specialists.

    AIOps undoubtedly will also have a profound impact on DevOps processes. As more IT operations become automated, the amount of time and effort spent managing IT infrastructure should decline.

    Interest in IT automation is on the rise in the wake of the COVID-19 pandemic. With many IT professionals working from home as much as possible, organizations are looking to automate rote IT processes to make them easier to manage remotely. In many cases, achieving that goal will require IT teams to rely more on tools that are accessed as a cloud service.

    Regardless of the path forward, it’s apparent the management of IT is about to be utterly transformed.

    It’s not that IT organizations will suddenly have a greater appreciation of IT automation. However, the pandemic has added more urgency to an IT transition that was already underway prior to the pandemic. The issue now is not so much whether to embrace IT automation as it is determining what can be automated for a consistent outcome across IT environments that frequently are made up of a diverse yet not especially well-integrated range of platforms.

  • Eggplant Injects AI Into Application Testing

    Eggplant Injects AI Into Application Testing

    Eggplant has infused its automated testing platform with deep learning algorithms that automatically identify and classify user interface (UI) elements such as buttons and text fields across any platform and then generate all the automation assets needed to instantly run a test.

    In addition, Eggplant has made it easier to visualize the overlap between common application failure patterns that can be used to automatically optimize testing.

    Finally, Eggplant has also revamped its own user interface to make it easier to deploy its platform across multiple test machines.

    Company CTO Gareth Smith said in the months and years ahead it’s apparent AI will be playing a much larger role in application testing. There are simply too many platforms on which application code is now being deployed for testing teams to keep pace manually.

    Each platform requires tweaks to the user interface to optimize the customer experience. At a time when many organizations are accelerating digital business transformation initiatives, the user application experience has never been more critical, he noted.

    Eggplant’s Digital Automation Intelligence (DAI) platform is designed to automate the entire testing lifecycle process as part of a larger best DevOps practice. The rate at which application tests can be built and run can be a bottleneck as applications are being built. The DAI platform looks to first automate the creation of UI tests and then launch them against application code to eliminate as much testing friction as possible, said Smith.

    IT teams also can track user behavior to create journeys based on user interactions with applications and store tests created using modeling tools provides by Eggplant in its cloud service. That approach not only accelerates the application development process, but it also serves to reduce the total cost of application development, which is critical at a time when IT budgets are squeezed because of the economic downturn brought on by the COVID-19 pandemic.

    As part of that effort, Eggplant provides DevOps teams with access to an application programming interface (API) through which they can self-service their own tests. That may not eliminate the need for a separate testing function within the application development process, but Smith noted it does make it easier for developers to discover potential issues on their own versus having to wait on application testers to discover them.

    Regardless of who conducts the testing, the rise of DevOps means there’s more application code to be tested than ever. As such, testing backlogs within many IT organizations are growing. The challenge is finding a way to conduct application testing that doesn’t result in application deadlines being missed. After all, when deadlines are missed, typically the first thing that gets scaled back is testing. Many DevOps teams convince themselves that any issues that come up can be addressed in the next update.

    Of course, once end users reject an application because issues were never discovered during testing, it’s not uncommon for an application to be rolled back. Rather than having to choose between those two evils, it might be better for all concerned to rely more on automation and AI to accelerate the testing process as much as possible.

  • Appian Infuses Low-Code Platform With AI

    Appian Infuses Low-Code Platform With AI

    Appian this week released an update to its low-code application development and deployment platform that employs artificial intelligence (AI) to recommend best next steps, including how to address cybersecurity issues.

    In addition, the latest version of Appian also makes it easier to incorporate robotic process automation (RPA) modules to automate rote tasks in addition to adding support for Google Cloud Document AI, which makes extracting documents for use within an application that employs RPA easier.

    Malcom Ross, vice president of product strategy for Appian, said the machine learning algorithms embedded with the Appian cloud platform make it possible to embrace best DevSecOps practices to accelerate the development of secure applications. With the rise of automated machine learning (autoML) it’s also becoming easier to automate the process of applying machine learning algorithms to application development and deployment, he said.

    While low-code platforms go a long way toward simplifying application-building, Ross said not enough attention is being paid to the deployment process. The goal is not necessarily to roll out more applications faster but to ensure whatever applications are deployed achieve a level of quality that eliminates the need to address cybersecurity issues post-deployment.

    Appian achieves that goal by running automated tests for an application, which now also include Appian Plug-ins to detect potential mismatches in plug-in versions or plug-ins that are missing.

    Ross said that when it comes to AI there always will be some trepidation. However, there are many manual tasks that many developers would just as soon see automated using AI. As AI becomes more widely relied on to drive consumer application experiences, the more acceptance there will be among IT teams to automate internal processes, he noted, adding cybersecurity is one area in which most developers will appreciate whatever help AI can provide.

    AI is not likely to replace the need for a DevOps team or cybersecurity professionals anytime soon. However, at a time when many organizations are massive amounts of economic pressure, it’s clear AI has a significant role to play in reducing the total cost of IT. Despite advances in IT automation, the cost of labor remains one of the most expensive elements of any IT budget. As such, IT organizations have a keen interest in making sure DevOps teams and cybersecurity teams are not wasting time on tasks that might be better handled by a machine.

    Obviously, the way AI systems and IT teams will interact with one another is still being defined. However, the one thing that is certain is platforms such as Appian will be making more recommendations about what needs to be done when. It may take some time for IT professionals to have confidence in those AI recommendations; however, in terms of mistakes that are routinely made it’s also apparent IT teams will soon be running out of excuses.

  • How DevOps Powered by AI and Machine Learning Is Delivering Business Transformation

    How DevOps Powered by AI and Machine Learning Is Delivering Business Transformation

    The use of artificial intelligence (AI) and machine learning (ML) is fundamentally changing the way we think about DevOps. Most notably, it is delivering a new form of DevOps that recognizes the need to have systems that are intelligent by design and underpinned by comprehensive security (DevSecOps). For many, this will be the crucial next step if DevOps is to shorten the software development lifecycle for all connected intelligent systems, ensuring the continuous delivery of secure high-quality software.

    By now, most organizations understand DevOps is a substantial discipline that they must adopt – according to Deloitte, organizations adopting DevOps see an 18%-21% reduction in time to market. By breaking down the silos between business and IT operations, DevOps can ensure consistent levels of productivity, efficiency and service delivery, all of which hold weight in these times of heightened uncertainty.

    To put it simply, DevOps can help businesses compete in already congested marketplaces. Through a foundation of continuous integration (CI) and continuous delivery (CD), organizations can ensure the customer receives the product they demand in the fastest time possible, while mitigating any elongated frustrations experienced from a lack of harmony between systems engineers and operations teams. Incorporating AI and ML into that DevOps strategy will take things to the next level.

    AI and ML Are the Next Evolutionary Step for DevOps

    Today’s companies are data-driven and being built as digital platforms. However, just 28% of organizations are currently succeeding in their digital transformation journeys. Increasingly, corporations are looking at what AI and ML can do to help them realize their transformative ambitions.

    Both the AI and ML markets are expected to experience huge growth over the next three to four years. Analyst firm IDC, predicts worldwide spending on AI systems will reach a href=”https://www.marketresearchfuture.com/reports/machine-learning-market-2494″>42.8% by 2024.

    What AI and ML reveals for these companies, and the increasing value of what Al, ML and devices can do together, are changing the way organizations view the world as they digitally transform. Coupling AI and ML with DevOps will lead to a significant shift in what DevOps gets involved with. 

    Primarily, it sets DevOps front and center of an organization’s wider digital transformation ambitions. If digital companies run on living data, then the development of these intelligent systems is an enticing environment for DevOps to prove its wider value to the organization like never before.

    Security Takes the Center Stage

    According to a TechTarget survey of key IT decision makers, cybersecurity and risk management were found to be the number one area for spending this year, with more than 53% of respondents saying they saw budgets increase in this area. And security is a crucial element that any organization employing a DevOps strategy should be considering. 

    DevSecOps is the simple premise that everyone involved in the software development cycle is responsible for security. In fact, the saying “security is everyone’s responsibility,” is a mantra that has been repeated often, and just as often ignored. Embedding security within every part of the development process, ensures security and compliance monitoring tools keep pace with the speed and agility that DevOps offers. It is a common understanding that security is the biggest block to the rapid and seamless development and deployment of systems, as security solutions have not traditionally been built to test and code at the speed DevOps requires. 

    Ultimately though, more automation from the start, reducing the need for manual configuration from security architect is where DevSecOps shines. This reduces the chance of misadministration and occurring faults, which can lead to downtime, and potential breaches or attacks. Quite often, the best ways to secure your organization is through simple hygiene factors, such as regular patching and software updates. 

    The reality is that every organization employing DevOps should really be approaching it as DevSecOps. Security is of ever growing importance to each and every organization and, through empowering it with AI and ML processes, it can be enhanced too, simplifying the processing of data to easily identify threats or potential vulnerabilities in the security makeup. 

    Reaching Beyond Human Capability

    The future will be made up of connected intelligent systems that span the intelligent edge, all the way to the cloud. The expectations of these systems are based on the lifecycle of a typical mobile application with constant feature enhancements. But, how do you know what to deliver next, especially when things are moving so quickly, and as developers are inundated with information and data from a number of different sources? This data deluge can be powerful, but it can also be overwhelming.

    Crucially, this is where the power of AI and ML comes into play. AI and ML will aid developers in making sense of the information housed across various data warehouses. In DevSecOps, there has always been an approach to automate everything, and AI and ML will be instrumental in automating the analysis and processing of data – a task that is now far beyond the capabilities of humans.

    Incorporating AI and ML will enable developers to better understand and use the data at hand. This will see developers understand not only the error, or the occurrence of a fault, but the detail of what happened in the run up to the fault – vastly reducing the chances of incurring that fault again. AI and ML will also be responsible for enabling the transformation from diagnostics to prognostics across any and all tools and systems, making it easier for developers to anticipate, identify and resolve faults or errors. 

    Increasingly, we’ll see organizations benefit and drive value from real-time insights. They’ll do this through AI and ML frameworks deployed on active systems to deliver optimizations based on real-time development, validation and operational data. This level of real-time actionable insight bolsters DevSecOps’ ability to achieve CI and CD, through improved, reduced mean time to resolution, easing the impact on operations and, ultimately, enhancing the end-user experience. It remains to be seen if incorporating AI and ML into the DevSecOps cycle will achieve the delivery of self-healing systems that can detect and resolve issues without the need for human intervention. With true AI, however, this might well be possible.

    The benefits and potential use cases of AI and ML are widely acclaimed, and while we are still some way from seeing their full potential, the capabilities they currently possess are revolutionizing many sectors. The fact is, if DevSecOps is to continue to keep pace with the vastly growing digital data economy, then it will become reliant on advanced technologies such as AI and ML. Data is powerful if used correctly, but with so much of it to process and analyze, that can be a challenge.

    A move toward secure automated processes is the progressive step that many organizations need to consider if they are to realize their digital transformation ambitions. Organizations cannot continue to do things the traditional way and expect to get the results that the new world is looking for.