devopsy.kinsta.cloud

Author: Mike Vizard

  • BMC Acquires StreamWeaver to Advance AIOps Strategy

    BMC Acquires StreamWeaver to Advance AIOps Strategy

    BMC has acquired StreamWeaver, a provider of a platform that makes it simpler to capture streaming data in real-time, as part of an effort to extend the BMC Helix IT operations platform infused with artificial intelligence (AIOps). Financial terms of the transaction were not disclosed.

    Margaret Lee, general manager and senior vice president for digital service and operations management for BMC, said StreamWeaver brings with it a wide range of integrations with multiple data sources that BMC will use to both train AI models and improve overall observability. While connecting to data sources is relatively straightforward, the challenge now is collecting that data in real-time in a way that is easily repeatable for IT operations teams, noted Lee.

    In general, AIOps continues to gain traction as IT environments become increasingly complex. Most IT operations teams are not going to be able to manage highly distributed computing environments without augmenting their IT staff with some AI, said Lee.

    Naturally, many rote tasks that were once performed manually by IT operations professionals will become increasingly automated. However, there will always be a need for IT personnel, noted Lee.

    Arguably, there may come a day when IT professionals will not want to work for organizations that don’t have an AIOps platform in place simply because the tasks assigned to them would otherwise be considered archaic, especially if they involve a series of tedious tasks that not many IT professionals really want to perform.

    At the same time, AIOps should reduce overall risk by making it easier for IT operations teams to proactively identify issues before they disrupt an application service. That’s especially critical as IT environments—increasingly with microservices-based applications constructed using containers—and serverless computing frameworks are being deployed with greater frequency alongside legacy monolithic applications.

    BMC has been making a case for AIOps as far back as 2017. However, since then, a wave of startups and IT operations incumbents have either built an AIOps platform from the ground up or updated an existing platform with AI capabilities. It’s difficult to assess which vendors are now ahead in terms of the AIOps race, but one thing that is certain is that IT organizations now have plenty of options.

    From a cultural perspective, however, not every organization is entirely comfortable with AIOps. While some IT professionals remain skeptical, others view AI as an existential threat to their existence. Most IT teams are also concerned about the potential impact any flawed AI model might have on IT operations—it’s one thing for a human to make a mistake, but an AI model making a mistake at scale could be catastrophic.

    There are few IT organizations that are embracing AIOps across the board, but most do recognize that AIOps platforms, in one form or another, are inevitably here to stay. After all, machine learning algorithms never forget anything they learn. Nor do they ever take a day off or decide to leave when offered higher pay. The challenge is figuring out exactly where the interface between IT staff and algorithms is going to be—not just today, but also in the near future.

  • Verica Launches Database for Sharing IT Incident Reports

    Verica Launches Database for Sharing IT Incident Reports

    Verica, a provider of an incident management platform, announced this week it is making available a database to make incident reports publicly available via a single repository.

    Courtney Nash, senior research analyst at Verica, said the Verica Open Incident Database (VOID) initiative is part of an effort to enable organizations to learn from outages and failures caused by software. Today, reports about such incidents tend to be difficult to find because they are scattered all across the internet, she added.

    That lack of transparency makes it difficult to compare and contrast incidents in a way that surfaces meaningful insights, noted Nash.

    Launch partners for the VOID include Indeed, NS1, Adaptive Capacity Labs, Security Scorecard and Auxon. Each will provide support to help fund the development, research and dissemination of knowledge generated by the project.

    The VOID contains more than just standard company postmortems or status updates, added Nash. Verica is committed to applying metadata to better understand how individuals, companies, media and others reacted during an incident.

    One early finding already derived from this initiative is that mean-time-to-resolve (MTTR) is proving to be an inaccurate metric due to the varied distribution of incident data. Other findings include the fact that only one-quarter of incident reports took the time to identify the root cause of an issue, while less than 1% took the time to study incident reports of near-misses.

    In general, there’s now more riding on the performance and availability of IT environments than ever. Digital business transformation became all the rage in the wake of the economic downturn brought on by the COVD-19 pandemic. As the economy continues to recover, businesses now expect IT to respond and pivot to changing conditions no matter how sudden.

    DevOps teams have, of course, learned to expect the unexpected by embracing modern incident management based on DevOps best practices that enable IT teams to adroitly respond to any outage or sudden degradation in application performance. The most important thing to remember about incident management is that practice makes perfect. While practice runs may never precisely mirror an actual crisis, organizations that have processes that are well-known by their IT incident teams and that practice ‘fire drills’ tend to respond both faster and better. After all, the more muscle memory there is to call on the more resilient the organization will be.

    It’s not clear to what degree IT organizations will be willing to share incident reports. However, there is an opportunity for IT professionals to come together that is similar to how engineers from different airlines collaborated to make flying safer for the general public, said Nash. In fact, with so many processes now dependent on software, it’s amazing there have not been more catastrophic events due to software issues. It’s even arguable that it’s only a matter of time before such levels of collaboration are mandated.

    In the meantime, the first step toward achieving that goal may simply be sharing a report with colleagues so they can learn from previous mistakes.

  • Perforce Charts Testing Course After BlazeMeter Acquisition

    Perforce Charts Testing Course After BlazeMeter Acquisition

    Perforce Software has acquired BlazeMeter from Broadcom as part of an effort to extend the scope of its testing tools beyond the Perfecto tools for testing mobile and web applications it acquired in 2018.

    Broadcom gained control over BlazeMeter after it acquired CA Technologies in 2018. CA Technologies acquired BlazeMeter in 2016. Terms of the latest acquisition involving BlazeMeter were not disclosed.

    Tim Russell, chief product officer for Perforce, said BlazeMeter adds a complete platform that spans everything from load and performance testing to application programming interface (API) testing. The goal is to enable DevOps teams to push continuous testing further left as part of an effort to accelerate the development and deployment of applications, added Russell.

    The need for continuous testing has become more pronounced as more organizations launch multiple digital business transformation initiatives, noted Russell. The real challenge will be finding ways to embed continuous testing within DevOps processes at a sufficient level of scale, added Russell.

    In fact, the quality of the digital experience those applications provide ultimately determines the degree to which those efforts are likely to succeed, which Russell noted is why so many enterprise IT organizations are reengineering their application testing processes.

    In general, organizations are taking two paths to shift testing further left. Some are giving application performance testing tools to developers to employ during DevOps sprints while others are trying to bring application testers into the application development process earlier. In some cases, organizations are doing both as part of a comprehensive effort to discover issues before applications are deployed in a production environment. Those issue are naturally more expensive to fix after an application has been deployed.

    Perforce, for now, expects to continue to make BlazeMeter and Perfecto available as a set of complementary cloud-based services with some cross-pollination of capabilities occurring over time, said Russell.

    In the longer term, it’s apparent that more artificial intelligence (AI)-based analytics will be embedded within cloud-based platforms that can collect enough data to build meaningful models of development processes. IT teams should also expect to see testing tools make greater use of computer vision algorithms to surface issues.

    One away or another, the entire application testing process is about to become a lot more automated as more organizations are simultaneously building multiple applications. That may not necessarily reduce the need for either a developer or tester to create and manage that process, but it does mean the amount of testing time required for each application should continue to steadily decline in the months and years ahead.

    Hopefully, all that effort will lead to better application experiences being provided to end users more consistently. There is nothing quite as frustrating for any application development team than to discover that 90% of the functionality of an application is not being used because end users are not able to easily surface it. Worse yet, of course, is when an application turns out to be too slow once it is deployed. In either case, it’s difficult to regain that initial end-user enthusiasm for any new application experience after it’s been lost, simply because no one thought through all the use cases that should have been tested much earlier in the application development life cycle.

  • ReleaseIQ Adds Orchestration Tool for Multiple Instances of Jenkins

    ReleaseIQ Adds Orchestration Tool for Multiple Instances of Jenkins

    ReleaseIQ has launched an orchestration framework for the open source continuous integration/continuous delivery (CI/CD) environment that makes it possible for DevOps teams to orchestrate pipelines across multiple instances of Jenkins.

    Seetharam Param, ReleaseIQ CEO, said ReleaseIQ Essentials for Jenkins is an implementation of an existing orchestration platform that enables DevOps teams to orchestrate pipelines across heterogeneous CI/CD platforms.

    The challenge is that organizations that have standardized on Jenkins typically have multiple instances being used by not only different IT operations teams but also owners of, for example, ancillary quality assurance processes. ReleaseIQ Essentials for Jenkins provides an alternative approach to the orchestration tools that CloudBees, the original developer for Jenkins, provides, said Param.

    Jenkins today is being advanced independently of CloudBees as an open source project managed by the Continuous Delivery (CD) Foundation, which is an arm of the Linux Foundation. Founding members of the CD Foundation include CloudBees along with Alauda, Alibaba, Anchore, Armory, Autodesk, Capital One, CircleCI, DeployHub, GitLab, Google, Huawei, JFrog, Netflix, Puppet, Red Hat, SAP and Snyk.

    ReleaseIQ Essentials for Jenkins optimizes execution of jobs and pipelines and also provides DevOps teams with a no-code graphical interface to build pipelines.

    DevOps teams can also import and execute Jenkins pipelines and jobs in addition to being able to pre-merge pipelines.
    Finally, DevOps teams are provided insights based on key metrics the platform automatically tracks on their behalf. Those metrics make it possible to quickly discover and fix pipeline issues across multiple Jenkins instances via console log aggregation, noted Param.

    It’s not clear to what degree DevOps teams that have standardized on Jenkins might swap out one orchestration tool for another. However, Param said more organizations are either wary of becoming locked into a specific CI/CD platform or now find themselves in a position where they need to continue employing multiple CI/CD platforms from different vendors. The ReleaseIQ platform provides a means for orchestrating pipelines regardless of how many different CI/CD platforms are employed, noted Param.

    It’s clear the need to embrace orchestration tools is becoming more pronounced. DevOps teams are spending too much time manually maintaining DevOps pipelines. The need for tools to automate the management of DevOps workflows is becoming critical as the number of application development projects that are simultaneously being launched continues to grow. Arguably one of the reasons that DevOps processes are not as widely embraced as they might be is the level of expertise required today to manage all the pipelines that make up a workflow.

    Of course, the level of DevOps maturity varies greatly by organization. It’s not uncommon for even different teams within the same organizations to have significantly different levels of proficiency. The challenge and the opportunity now is to provide those DevOps teams with tools that operate at a high enough level of abstraction to make DevOps accessible to every IT professional regardless of the level of programming expertise they may or may not have.

  • Perforce Melds Artifact Package Manager with Version Control Platform

    Perforce Melds Artifact Package Manager with Version Control Platform

    Perforce Software has added a universal package manager, dubbed Helix Artifacts, to its portfolio that enables users of its Helix Core version control platform to store and track source code.

    Gerhard Krüger, product manager for Helix Core digital asset management at Perforce Software, said the goal is to enable DevOps teams that employ Helix Core to access a package manager without having to exit Helix Core.

    That approach also provides a more secure and cost-effective way to store, consume and distribute artifacts that also serves to improve developer productivity, he added. The ability to manage artifacts on top of a version control platform also means DevOps teams will not need to set up additional storage infrastructure or configure separate security permissions just to distribute software packages, noted Krüger.

    Perforce

    Helix Artifacts is a self-hosted package manager that functions as a translator for packages such as NuGet, Maven or Docker that enables them to communicate directly with Helix Core as well as build systems and integrated developer environments (IDEs). Helix Artifacts is made available via a per-server subscription model that costs substantially less than rival approaches to managing artifacts, Perforce Software claims.

    Most DevOps teams are trying to strike a balance between employing best-of-breed tools to build applications and the need to reduce complexity and, ultimately, cost. As such, any time a tool becomes a feature of another platform that a DevOps team uses, there will be a review of the processes being employed. Many DevOps teams are trying to reduce the amount of time they spent maintaining tool integrations.

    There’s also now a lot more focus on software supply chain security. Each new tool generally creates yet another set of credentials that need to be protected as cybercriminals continue to aggressively attempt to inset malware into applications before they are deployed in a production environment. It’s not clear to what degree DevOps teams are looking to consolidate toolchains, but given those rising security concerns, it’s only a matter of time before security teams start to ask why the DevOps toolchain attack surface is so wide.

    In the meantime, the number of organizations that have widely embraced DevOps best practices across the enterprise continues to steadily grow. Over the past decade, most organizations have had at least one application development team embrace DevOps. However, as the pressure to build software to drive digital business transformation initiatives has steadily increased since the onslaught of the COVID-19 pandemic, the need to apply DevOps best practices to accelerate application development has become more pronounced. As a result, more organizations than ever are looking to standardize their application development efforts on a smaller number of platforms to reduce friction.

    It’s too early to say whether the rate at which applications are being developed and deployed since the start of the pandemic has actually increased. The one thing that is certain, however, is the bulk of organizations are now trying to accelerate like never before.

  • Sumo Logic Extends Observability Scope and Reach

    Sumo Logic Extends Observability Scope and Reach

    During the online Illuminate user conference, Sumo Logic revealed how it is integrating capabilities it gained with the June 2021 acquisition of Sensu, a provider of open source monitoring tools, with its continuous intelligence platform in addition to adding support for data collected via open source OpenTelemetry software.

    Sensu Go, a set of tools for implementing monitoring-as-code, is now a core part of the Continuous Intelligence Platform. The company is also now making available in beta Sumo Logic OpenTelemetry Distro and Ecosystem Support, a single agent through which all logs, metrics and traces can be collected from instrumented applications. Sumo Logic is also supports the AWS OpenTelemetry Distro, an offering from AWS that provides similar capabilities. Sumo Logic has also increased the capabilities of Sumo Logic Free to include monitoring-as-code tools created Sensu.

    Bruno Kurtic, vice president of product and strategy, said as responsibility for managing applications shifts left toward developers it’s critical to provide observability capabilities that can be programmatically infused into applications. As part of that effort, Sumo Logic is now also offering Sensu Plus, which provides access to an analytics engine for the data the platform collects.

    Sumo Logic is also adding machine learning algorithms to provide more context to the alerts the platform generated via an Alert Response feature that is part of the core platform.

    The company is also adding support for the operator software that Red Hat created to make it simple to deploy Sumo Logic Continuous Intelligence Platform alongside the Red Hat OpenShift platform for building and deploying applications on top of Kubernetes clusters. In addition, Sumo Logic is also providing integration with a total of 38 either new or updated observability applications that integrate with a wide range of third-party platforms.

    Finally, Sumo Logic is extending the security observability platform it provides based on its platform to add support for containers and serverless computing functions alongside virtual machines and integrations with third-party endpoint detection and response (EDR), network detection and response (NDR), threat intelligence and managed detection and response (MDR) platforms. The company has also added or updated 26 applications for specific third-party platforms.

    Kurtic said Sumo Logic makes it possible for developers and IT operations teams alike to observe an IT environment on an end-to-end basis via a single pane of glass. In some cases, the members of an IT operations team will require a user interface to manage that environment. Developers, meanwhile, might prefer an application programming interface (API) through which observability data is made accessible from within a continuous integration/continuous delivery (CI/CD) platform.

    Regardless of the type of user, however, IT teams should not have to hire their own data science team to analyze observability data. Instead, the observability platform should embed machine learning algorithms that surface all the benefits of data science without the need to hire additional headcount, he said.

    One way or another, DevOps teams will soon routinely benefit from the additional levels of context that observability platforms provide. The challenge will then be determining how best to fix a raft of issues that many DevOps teams today simply didn’t know existed.

  • CloudBees Acquires Neuralprints to Shift Compliance Left

    CloudBees Acquires Neuralprints to Shift Compliance Left

    At the online DevOps World 2021 conference today, CloudBees revealed it has acquired Neuralprints to provide the core technology for CloudBees Compliance, a real-time compliance and risk analysis platform that it will roll out in the first quarter of 2022.

    At the same time, CloudBees has enhanced the feature management capabilities of its namesake continuous integration/continuous delivery (CI/CD) platform to provide greater visibility into and control over feature flags that are now widely used to add new capabilities to applications.

    Prakash Sethuraman, chief information security officer (CISO) for CloudBees, said CloudBees Compliance will make it possible for DevOps teams to continuously enforce compliance policies as part of an effort to better secure their software supply chains. Those DevOps teams will be able to enforce compliance across code, binary artifacts, data, identity and infrastructure environments in a way that provides developers and IT operations teams with instant actionable feedback to enable issues to be addressed long before an application is deployed in a production environment.

    CloudBees Compliance is based on a mix of open source and proprietary technologies developed by Neuralprints and will be made available for on-premises IT environments as well as via a software-as-a-service (SaaS) application. The goal is to enable organizations to manage compliance as code as part of any effort to shift more responsibility for application security further left toward DevOps teams, said Sethuraman.

    CloudBees Compliance

    A survey of 500 C-level executives published today found nearly half (45%) admit that initiatives to secure their software supply chains are halfway or less-than-halfway complete. Nevertheless, 95% claimed their software supply chains are secure (95%), with more than half (55%) posting they are very secure.

    A full 93% said they are prepared to deal with an issue such as ransomware or a cyberattack on their supply chain. However, nearly two-thirds (64%) admitted they are not sure who they would turn to first if their supply chain was attacked; with 58% admitting that, if they experienced an attack, they have no idea what their company would do. An equal percentage (64%) said it would take more than four days to fix the problem if they did experience an issue.

    Almost all C-level executives (95%) said they think more about securing the supply chain now than they did just two years ago, and 92% said a security issue would impact their brand. A total of 83% said security issues cause their developers to drop everything to review code, with 82% noting that caused the organization to spend less time on innovation.

    On the plus side, 95% of executives said container images are checked for high or critical vulnerabilities, while an equal number said automation access keys are set to expire automatically. A total of 92% said their company only accepts commits signed with a developer GPG key, while 90% said dependencies to trusted registries are limited at their organization, while 89% said administrative access to CI/CD tools is restricted (89%).

    Of course, what C-level executives believe and what actually occurs inside an organization are not always one and the same. A series of high-profile software supply chain breaches resulted in a number of organizations reviewing their software supply chains. At the same time, the Biden administration has issued an executive order requiring all federal agencies to conduct similar reviews.

    One way or another, most organizations will soon find themselves embracing a wider range of DevSecOps best practices to ensure the integrity of software supply chains no matter how secure they may think their software development platforms are today.

  • Red Hat Extends Scope of Ansible Automation Ambitions

    Red Hat Extends Scope of Ansible Automation Ambitions

    During the online Ansiblefest, Red Hat announced the availability of a preview edition of Red Hat Ansible Automation Platform 2 that is intended to make it easier to automate IT processes at scale.

    Richard Henshall, senior manager for Ansible product management at Red Hat, said previously most IT teams have been automating individual processes, but as organizations become more adept at creating playbooks to automate processes, they now need to find ways to manage multiple automations that are often dependent on one another.

    Ansible Automation Platform 2 also enables IT teams to automate processes that span both monolithic as well as cloud-native applications based on containers using an automation controller, formerly known as Ansible Tower, that is more tightly integrated with Red Hat OpenShift, an application development and deployment platform based on Kubernetes, using Red Hat Operator tools. There is also an automation mesh capability that connects disparate automation components together and provides status checks.

    Ansible Automation Platform 2 also introduces automation execution environments within Ansible Engine that make it possible to create self-contained automation spaces that can be easily replicated and repeated across an organization.

    Red Hat also includes an automation content navigator to helps teams validate that automation content is working as it should across even the largest environments, along with updates to the Red Hat Insights analytics service that enable issues that might occur with automation controllers to be surfaced and eliminated by automatically applying policies.

    Henshall said going forward, IT teams should expect to see Red Hat focus more on enabling events to be the catalyst for triggering automation. Today, most automated processes begin with an IT administrator triggering the process. However, as digital business transformation initiatives mature, it’s become apparent there will be a need to trigger automation when specific events occur.

    Henshall said while there are still many organizations that are just starting to automate IT processes, there are also many that are entering the next phase of maturity. As such, they now require an automation platform that makes it simpler to manage automation at scale.

    In general, Ansible is designed to make it possible for developers and IT operations teams alike to declaratively create automation without much programming expertise required. However, creating a playbook that works reliably still requires a deep understanding of the processes that are being automated. Most IT teams would be well advised to closely monitor processes when they are first automated to make sure they are performing tasks as anticipated.

    Ultimately, it’s now a question of when rather than if most IT processes will be automated. DevOps teams, of course, have always been at the forefront of automation, but now it’s clear that as IT environments become more complex—with the rise of both the cloud and edge computing platforms—enterprise IT teams need to embrace automation more aggressively. The biggest barrier to automation is simply finding someone who has the passion to automate a process to lead the charge, said Henshall.

    Naturally, most of those efforts will initially focus on repetitive, tedious and manual IT tasks, but the potential to automate IT processes at levels of unprecedented scale has never been greater.

  • LogDNA Previews Streaming Module for Observability Platform

    LogDNA Previews Streaming Module for Observability Platform

    LogDNA is making available on a limited basis the ability to process streaming data to its observability platform as part of an effort to give DevOps teams more control over log data.

    Tucker Callaway, LogDNA CEO, said the data ingestion pipeline that LogDNA already provides can ingest, parse and normalize massive amounts of structured and unstructured log data. LogDNA Streaming adds the ability to automatically send log data to any application or analysis tool.

    That capability also helps reduce storage costs by limiting the number of copies of log data that might otherwise be created, he added.

    LogDNA also expects that DevOps teams will also be able to analyze data in flight as part of an effort to make it simpler to surface issues even before log data is stored, noted Callaway.

    While there are multiple streaming data platforms available, Callaway said LogDNA opted to build one on its own to meet the requirements of an observability platform. Ultimately, the goal is to shift observability left at the data level to provide more granular insights in real-time, he said.

    That capability will also lay the foundation for a wide range of autonomous processes that would be driven by the observability data collected via LogDNA, added Callaway.

    LogDNA

    Observability has always been a core tenet of any DevOps best practice. Most DevOps teams typically focus on some form of continuous monitoring to more proactively manage application environments. However, it can still take days, sometimes weeks, to discover the root cause of an issue. Monitoring tools are designed to consume predefined metrics to identify when a specific platform or application is performing within expectations. The metrics tracked generally focus on, for example, resource utilization.

    Observability combines metrics, logs and traces—a specialized form of logging—to instrument applications in a way that makes it simpler to troubleshot issues without having to rely solely on a limited set of metrics that have been pre-defined to monitor a specific process or function. DevOps teams can more easily employ queries to interrogate data in a way that makes it easier to discover the root cause of an issue. An observability platform correlates events in a way that makes it easier for analytics tools to identify anomalous behavior indicative of an IT issue.

    It’s now only a matter of time before providers of the platform widely employ machine learning algorithms to analyze machine data faster. There may even come a day when so-called “war room” meetings—that today need to be convened to identify the cause of an IT issue via a painstaking process of elimination—are no longer required.

    In the meantime, the percentage of applications that are instrumented in an IT environment needs to increase to achieve that goal. Fortunately, thanks to the rise of open source agent software, the cost of instrumenting applications is starting to decline. At the same time, DevOps teams should expect to see a marked increase in the volumes of incoming data, and they will need to find somewhere to store that data as well as determine the length of time it should be retained.

  • Relyance AI Shifts Compliance Left Using ML Algorithms

    Relyance AI Shifts Compliance Left Using ML Algorithms

    Relyance AI emerged from stealth this week to unveil a namesake platform for managing privacy and data governance in real-time within the context of a larger DevOps workflow.

    Fresh off raising $30 million in seed capital, Relyance AI co-CEO Leila Golchehreh said organizations can now quickly and accurately answer essential privacy questions and ensure compliance and governance by mapping requirements directly to their codebase. The platform provides transparency and visibility all the way down to the source code in real-time using machine learning algorithms, she added.

    Relyance AI co-CEO Abhi Sharma also noted most legal and data protection teams today have limited visibility into digital operations. Developers and data scientists are constantly creating and updating applications. By the time compliance teams gather the data required, it’s obsolete, said Sharma.

    The Relyance AI platform analyzes contractual requirements against the operational reality as determined by data processing in real-time. Compliance teams can then address privacy issues as they arise, added Sharma.

    At the core of the Relyance AI platform are five integrated components that first inventory assets by connecting to application programming interfaces (APIs); generate universal records of processing activities (ROPAs) by scanning code and understanding how data is being processed; share alerts when issues are detected; provide views into data life cycles to highlight data subject access request (DSAR) exceptions and extensions and address vendor life cycle management to ensure third-parties are complying with policies.

    Relyance AI

    Sharma said compliance, much like security, is shifting further left toward application development as organizations look to eliminate issues before they arise in a production environment. The Relyance AI platform is designed to be integrated with continuous integration/continuous delivery (CI/CD) platforms that are the core of modern software development life cycle processes, he added.

    Undoubtedly, as the worlds of compliance and application development collide, there will be cultural challenges similar to what is now occurring with the rest of DevSecOps best practices that require more collaboration between developers and security teams. The challenge is compliance officers tend to be even further removed from application development than their IT security counterparts.

    However, it’s now only a matter of time before compliance becomes code. Modern applications already make use of the Open Policy Agent, for example, to embed compliance rules in applications. AI platforms should make it simpler to identify compliance issues as they arise so that they can be addressed before any infraction ever occurs.

    DevOps teams, of course, have a vested interest in managing compliance as code. It’s not uncommon for DevOps teams to race to build and deploy an application only to encounter a wave of compliance issues that need to be addressed by recoding elements of the application. Not surprisingly, those compliance issues often result in application delivery deadlines being missed. Machine learning algorithms provide an alternative to the current raft of documents that compliance officers create, but not enough other members of the IT team read those, much less understand them.