devopsy.kinsta.cloud

Category: IT as Code

  • Codenotary Previews Secure SBOM Creation Service

    Codenotary Previews Secure SBOM Creation Service

    Codenotary today made available a preview of a centralized repository service for generating and storing software bills of materials (SBOMs) that makes it simpler to securely share them as necessary.

    Moshe Bar, Codenotary CEO, said SBOMCenter will make it easier for organizations to operationalize SBOMs that are being created with greater frequency as the need for increased focus on securing software supply chains grows.

    Currently available as a free trial, Codenotary’s SBOMCenter provides a way to create and store SBOMs in a way that ensures they have not been tampered with, noted Bar.

    Implementing SBOMs can be a cumbersome process that can require changes to existing software development and procurement practices. The SBOMCenter provides a service for accessing SBOMs that are continuously updated using an immutable open source database that supports multiple SBOM formats and a tool the company developed that automatically generates SBOMs by adding a single line of code. That approach should make it simpler to identify what software components are running in a production environment any time a new zero-day vulnerability is discovered.

    In a future update to SBOMCenter, the company said it plans to add vulnerability scanning, risk exposure scoring, alerts on newly discovered vulnerabilities and a policy enforcement engine that can be integrated with DevOps pipelines.

    Awareness of the need for SBOMs has increased dramatically since the Biden administration’s executive order made it clear that federal agencies would require them from any software provider starting next year. Many enterprise IT organizations are likely to follow suit as part of a larger effort to better secure software supply chains in the wake of a series of high-profile cybersecurity breaches.

    More recently, a National Cybersecurity Strategy paper published by the White House calls for increased liability for software developers that fail to exercise due cybersecurity diligence when building applications. It’s not clear if there will ever be a law on the books that would enable penalties to be applied, but the general mood is clearly shifting toward holding organizations that build software more accountable for cybersecurity issues that might arise.

    It’s not yet clear how far down the path organizations are toward operationalizing SBOMs. Gartner predicted that, by 2025, a full 60% of organizations will be employing SBOMs. In theory, those organizations should be able to prevent software with unverified components from being deployed in production environments and identify which applications currently running have significant flaws.

    On way or another, application providers will find themselves disclosing more about how their software is constructed. The issue now is finding a way to accomplish that goal in a way that respects intellectual property rights. As such, application providers need to find a secure way to share SBOMs with customers that will commit to making sure the contents of those SBOMs stay private. After all, a little cooperation from the coalition of the willing is likely to have a more positive impact than a law that is likely to be challenged.

  • Device42 Brings Power and Carbon Emission Tracking to IT Asset Management

    Device42 Brings Power and Carbon Emission Tracking to IT Asset Management

    Device42 today made available a power and CO2 Summary Dashboard to its agentless platform that employs machine learning algorithms to discover and track IT assets.

    Raj Jalan, Device42 CEO, said the dashboard makes it possible for IT teams to combine the tracking of assets and associated dependencies with a capability that enables them to reduce power consumption costs and levels of carbon emissions.

    It’s not clear yet how carbon emission tracking will be integrating into IT management frameworks, but as sustainability becomes a more pressing issue, it’s only a matter of time before IT teams will be required to monitor power consumption and associated carbon emissions more closely, said Jalan.

    The Device42 dashboard simplifies that task by identifying carbon emissions by location, business unit, vendor or application. Armed with that data, it then becomes possible to consolidate servers and applications or move workloads to more efficient platforms, added Jalan.

    Arguably, the biggest challenge when it comes to managing IT infrastructure is the number of organizations that are still using spreadsheets to do so. The Device42 approach to discovery and assessment uses the application programming interfaces (APIs) exposed by cloud services providers and IT vendors. Once mapped, it then becomes possible for IT teams to use a search function to uncover, for example, dependencies between multiple services.

    In theory, it should be possible for DevOps teams to also programmatically implement policies based on the Device 42 data to move workloads in way that reduce IT infrastructure costs. Over the years, many organizations have lost track of how workloads have been deployed in, for example, cloud computing environments—only to discover a surprise expense at the end of the month.

    In fact, one of the hottest segments of IT these days is the emergence of financial operations (FinOps) as a discipline to control cloud computing costs. Fundamentally, FinOps is the discipline that promotes a shared responsibility for an organization’s cloud computing infrastructure and costs. Rather than having disparate procurement teams working in silos to identify and approve costs, business, financial and IT leaders establish policies and best practices for usage that are programmatically enforced.

    It’s still early days as far as the adoption of FinOps is concerned, but adding that capability first requires a deep appreciation for what IT assets are being used to run which workloads. From there, it then becomes possible to not only optimize usage but also reduce both electricity costs and carbon emissions.

    Of course, the tracking of carbon emissions right now is a bigger issue in Europe and elsewhere than it is in the U.S. However, a global framework for trading carbon credits is now impacting IT teams as organizations look to minimize their carbon footprints. Sustainability has become an economic issue.

    In the meantime, DevOps teams should expect that soon, power consumption and carbon emissions will be added to the list of metrics they are measured on.

  • Platform Reality Has a Surprising Amount of Detail

    Platform Reality Has a Surprising Amount of Detail

    Developer platforms and the platform teams that create them have been the center of attention for quite some time now. There have been many (wildly different) opinions about the subject, ranging from how bad the notion of a platform team is to how essential platforms are to make an organization’s DevOps journey to cloud native successful.

    Since early this year, the CNCF TAG App Delivery team has been working on a whitepaper about platforms. It explains what platforms are, why they are needed and how platform teams might work to create them. Furthermore, there’s an abundance of end-user talks at conferences that show how platform teams and their platforms were essential in enabling developers at scale. Even on the KubeCon + CloudNativeCon keynote stage, we’ve seen success stories of platform teams like the one at Mercedes Benz.

    Developer Platforms Should Enable Developers

    There is no silver bullet to successful platform teams and platforms. However, it is important to understand that the main goal of a developer platform is to enable developers. It is essential for platform teams to focus on delivering that value, which is why we are seeing a trend to introduce actual product ownership and product management roles to platform teams.

    The CNCF whitepaper stated that the three core jobs of a platform team as follows:

    1. Research platform user requirements and plan a feature roadmap.
    2. Market, evangelize and advocate for the platform’s proposed values.
    3. Manage and develop interfaces for using and observing capabilities and services including portals, APIs, documentation and templates and CLI tools.

    I couldn’t agree more. But the reality of day-to-day business for many platform teams means they rarely get to focus on those three jobs.

    Reality Has Detail

    The situation reminded me of a blog post I read a few years ago, titled “Reality has a surprising amount of detail.” The author explained how it is easy for people to get stuck even when faced with a task that might look simple from the outside. They explain how a task as simple as building wooden stairs can, in its many subtasks, become quite complex. Furthermore, when faced with reality, even the tasks that seemed plannable get more complex and need real-life adjustments.

    Most engineers have had analog experiences in their day-to-day work. We can translate this notion into platform engineering quite well. Building a developer platform sounds straightforward at the onset. However, once we get into it, it becomes a rabbit hole of figuring out the CNCF landscape, adding company-specific glue, compliance, security, fixing bugs, getting deep into the open source projects and sometimes, faced with the reality of open source, even becoming contributors and maintainers of such projects.

    Now, I’m not saying that it’s all bad. All these things can be great and rewarding in their own way. Especially with a community like the CNCF, participating and contributing to open source can be a very rewarding experience. You’ll make lots of friends, learn DevOps and development skills and might even further your career significantly.

    While it’s a lot of work, there is a risk that every platform team basically reinvents the wheel. This is because an organization’s requirements seem too large to rely on a single product (or even a suite of products). We end up with bespoke platforms that need a lot of attention. These platforms also have platform teams that don’t have enough time to focus on the higher-level jobs that they were tasked with.

    Platform Evolution

    Over the years talking to and working with platform teams we have built a model of prototypical platform evolution and its risks at each stage.

    Usually, a platform team starts with the task of building a developer platform to enable the product teams to build great software and deliver value to the end users.

    As they build the platform from the first Kubernetes clusters to observability tooling to security, the backlog for features constantly grows. Even with a growing platform team and sub-teams for certain technology areas, there’s a risk that some of these backlog items never get attention. Especially, delayable, non-critical, daunting tasks (like upgrading to the next Kubernetes release) often fall victim to this problem.

    Next, when it comes to operations, the platform team needs to limit the possibilities and breadth of what they offer. Operations responsibility for functionality that is outside of those limits, (for example, a specialized data store) goes back to the product teams themselves, which results in developer distraction.

    Even with a wide coverage of the platform, Day 2 and operations, the next big task for the platform team is advocating for adoption. This is not just marketing for their platform, but also means the platform team has to build user-friendly interfaces and documentation. The risks and alternatives we often see here are that product teams build their own mini-platforms and systems that result in shadow IT. Another common risk is ending up with several competing platforms within big enterprises that are hard to integrate and centralize, which in turn leads to inefficiencies.

    Once we get over the adoption hurdles, it is the platform team’s job to deliver value and high-level capabilities to the product teams’ developers. However, at this stage, often the platform teams have grown and might encompass several sub-teams for technology areas. Additionally, some areas like security might already be under central IT control. The risk we see here is that the capabilities offered are reduced to technology silos. They might cover all areas like observability, connectivity, security and release engineering, but to achieve real maturity, a platform team needs to offer capabilities that span several of those areas.

    Many of the advanced capabilities that we see with more mature end users cover several or all technology areas. Think about a simple high-level capability, like progressive rollouts. It starts with release engineering capabilities to build and deploy the software. From there, we need connectivity capabilities to dynamically route traffic to the new service. Then we need observability capabilities to observe our new version and from there have automated feedback loops to roll back or go forward with the rollout to production. And if that wasn’t enough, as we’re a security-aware organization, we want the whole software supply chain that facilitates this to be secure and trusted.

    Question for the curious cloud native enthusiasts: How many tools do you need to integrate to achieve the above “simple” high-level capability in an automated way? And this is only one of many capabilities.

    PaaS-Like Platforms Without the Downsides

    So, while platforms and platform teams are definitely the way to go, we need to be aware of the amount of work and effort required.

    We need to be aware of the risks and strive towards building highly integrated platforms that deliver high-level capabilities to the product teams.

    However, cloud-native developer platforms, while having similar goals to the PaaS systems of the past, should not follow the same approach of full abstraction. If we just abstract away into bespoke systems that hide everything from the user, we go back to the world of PaaS where at some point the end-user developers hit limits and build workarounds around the platforms.

    Additionally, if we build these bespoke internal platforms, we’ll be stuck with them and need to maintain and evolve everything by ourselves.

    Or, to go back to the blog post I referenced earlier, “At every step and every level there’s an abundance of detail with material consequences.” And we have to remember what we care about. The way to avoid both of these downsides is to bet on open source and stand on the shoulder of giants.

    By building our platforms on open source and using as many of the established or soon-to–be-established standards of our community we can avoid our platforms becoming bespoke. Recent developments like Cluster API, Gateway API and sigstore are some good examples.

    Furthermore, we need to make our abstractions poke-able. While our platforms should come out of the box with user-friendly interfaces and sensible production defaults that enable new product teams to onboard quickly and get their jobs done without having to bother with deep understandings of the underlying technology, we need to keep them open to mature users that know their way around and want to tweak it to their needs. We achieve this by giving mature users the option to access said open standards and APIs safely.

    This is the strength that we gain from a community like the CNCF, so we should use it to build great platform products that stay true to the core of open source and strive to make our product teams as happy and successful as possible. And remember to be happy with ourselves as successful and appreciated platform teams.

  • The Fallacy of Continuous Integration, Delivery and Testing

    The Fallacy of Continuous Integration, Delivery and Testing

    We know that continuous integration and continuous delivery (CI/CD) have become a DevOps best practice. And many have learned that by adding continuous testing (CT), they can create a virtuous loop, ensuring perpetual code quality and security. They’re not wrong.  Yes, testing continuously is good practice, but some incorrectly equate the concept of continuous with monitoring, thinking they’re able to see everything that could eventually affect a customer’s experience (CX) with their software application. That’s a fallacy. Many of the most glaring blind spots that wreak havoc on CX exist outside the environment that CI/CD/CT is able to “see.” CI/CD/CT, even when supported by application performance monitoring (APM), doesn’t and cannot monitor for and alert to blind spots across the internet stack. And there are many. Only internet performance monitoring (IPM) can take CI/CD/CT as input streams and shift to continuous resilience as a measurable outcome.

    How CI/CD/CT Typically Works

    As customer requirements evolve, software development teams write code that triggers a new software build. In the continuous model, each new build moves to a runtime environment for integration and quality assurance and, ultimately, is deployed to end users across public or hybrid clouds. This is an application-specific view of the world.

    Since quality assurance is part of the typical continuous process, monitoring is an assumed benefit or outcome. This assumption might be because many now complement their CI/CD/CT processes with APM. What is achieved with APM in this approach is testing, not monitoring. While testing provides a better picture of performance at the application level, it doesn’t provide full visibility into the blind spots that exist between the application layer and the end user.

    APM Isn’t Enough

    While APM tools focus on code—including everything that negatively affects an application such as database wait times and inefficient code—IPM focuses on the internet, which has become your network. IPM monitors what impacts your customers, workforce or application (API) experiences across the internet.

    While APM and IPM may seem similar, they are nothing alike. They may share common tools, such as using synthetic agents, but the difference is in what they monitor and how. In this age of highly distributed applications, that difference means everything.

    Where APM can “simulate” what a user may see from cloud location to cloud location, which can confirm application performance, this simulation isn’t true or comprehensive monitoring because it is blind to network-related problems with end-user experience, which are vast. Customers and employees each have a unique internet performance fingerprint and access applications from myriad devices across multiple locations and network infrastructures, not through the individual cloud locations that APM synthetics simulate.

    IPM approaches synthetic testing more holistically, replicating multiple user journeys across a much more complex Internet infrastructure to proactively flag performance issues before they impact end users. This true monitoring takes place 24/7, not as point-in-time tests.

    Forensics vs. Monitoring

    With APM and similar observability solutions that embrace real user monitoring (RUM), it’s important to focus on what monitoring really means. APM passively collects data that is a proxy for user experience, enabling postmortem assessment of causes and possible fixes, but after the fact. Observability looks at logs, metrics and traces and provides an assessment of whether users are having a good experience or why they did not. Neither solution by itself will prevent poor experience, and many fixes won’t take place until a postmortem analysis feeds the next iteration. The poor experience has already done its damage, and the costs are already sunk.

    With thousands of potential blind spots across the globe that can slow or disrupt applications, from ISPs and wireless carriers to CDNs, it’s not hard to grasp the increasing and differentiated importance of IPM. Where the combination of CI/CD/CT with APM can proactively prevent deployment of poor code that would affect user experience, only IPM is continuously replicating – even more valuable than monitoring–millions of experiences across a dynamic multi-node network where the unexpected is now expected.

    Internet Resilience Vs. Retrospection

    Businesses today understand the importance of resilience: It’s a board-level concept. Any business that has lost market share and market cap during sustained downtime or poor user experience understands what’s at stake: Millions if not billions of dollars. IPM is squarely focused on “internet resilience,” and its pillars are availability, reachability, performance and reliability. For businesses reliant on the cloud and employers operating with highly distributed workforces, retrospection alone is a risky proposition. Now that the internet is the new corporate network, internet resilience is a new organizational mantra.

    The Takeaway

    Whether your organization embraces CI/CD/CT already or is rethinking its approach to DevOps, this article should give you pause. Your job–perhaps as part of a larger team–is to catch performance issues and potential disruptions with your application before client impact is realized. Without IPM, only part of that job is being done.

    Why is this so important? Even conservative estimates put the cost of an outage at $6,700 per minute (Gartner) and the “cost of slow,” where something causes an application to perform poorly and it goes unnoticed, is likely significantly higher because it happens all the time. The fallacy isn’t that there’s value in CI/CD/CT–there is. The fallacy is that you’re in control of the entire CX of your internet-deployed products without monitoring the internet stack and ensuring internet resilience. Only by adding IPM can you have this assurance.

  • Atlassian Embeds Generative AI Across Cloud App Portfolio

    Atlassian Embeds Generative AI Across Cloud App Portfolio

    At its Team 23 conference today, Atlassian announced it is adding an artificial intelligence (AI) capability to the enterprise editions of its cloud portfolio to automate a wide range of routine tasks.

    Sherif Mansour, distinguished product manager for Atlassian, said Atlassian Intelligence uses natural language to drive a virtual agent that resolves routine help desk issues from within Slack or Microsoft Teams. Atlassian Intelligence can also identify incidents that have been previously resolved along with issues that are related to one another.

    In addition, IT teams can also leverage Atlassian Intelligence to create summaries for projects being tracked using the company’s Jira project management software, noted Mansour.

    The overall goal is to streamline incident management processes while also making it simpler for application stakeholders to collaboratively participate in software development, he added.

    Generative AI capabilities are likely to be infused into almost every imaginable application going forward, but Atlassian is clearly at the forefront of an effort to leverage AI to improve productivity. For example, instead of spending time handling routine issues that increase the amount of daily toil, IT teams should be able to spend more time resolving more complex issues, noted Mansour.

    The cumulative effect of AI should result in a significant decrease in the backlog of tasks that many IT teams would otherwise have to manually perform, he noted.

    It’s not clear just yet how much generative AI will impact the way software is developed and how IT is managed, but it’s already apparent that fundamental change is on the way. The Atlassian Intelligence approach is intended to augment the capabilities of IT teams so they can do more with the same number of or fewer personnel.

    The challenge IT teams will face is identifying tasks they couldn’t previously accomplish simply because there wasn’t enough time available.

    Fundamentally, generative AI changes the way humans interact with machines. Instead of requiring a developer to create a level of abstraction to communicate with a machine, it’s now possible for machines to understand the language humans use to communicate with each other. That makes it possible for anyone to make use of large language models used to create generative AI platforms to, for example, automate a routine task or, one day soon, create code.

    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 IT organizations, the ultimate impact should involve less drudgery as many of the tasks that make managing IT tedious are eliminated.

    Hopefully, as IT continues to evolve, there will still be plenty of demand for human expertise. For better or worse, the nature of the expertise required, however, will undoubtedly be at a much higher level than it has been for the past four decades or more.

  • Report: Most IT Teams Can’t Fix Open Source Software Security

    Report: Most IT Teams Can’t Fix Open Source Software Security

    Lineaje, a provider of a platform for securing software supply chains, today published an analysis of 41,989 open source components embedded in the top 44 popular projects managed by the Apache Software Foundation (ASF). That analysis found more than a quarter (26%) of vulnerabilities are not patchable by the application development team that deployed them.

    In addition, the report found that 64% of the vulnerabilities analyzed have no patch as of yet. Overall, 68% of the vulnerabilities analyzed are due to dependencies created when an open source software project included a component or package developed by another open source project maintainer.

    Lineaje CEO Javed Hasan said that means that 90% of open source dependencies are transitive, in the sense that they are created when maintainers of a project included a vulnerable open source component created by another entity. Only 10% of the vulnerabilities discovered by Lineaje are the result of a dependency that an application development team could actually address on their own, he added.

    Overall, the report found a full 82% of components that are relied on to build open source software are inherently risky due to vulnerabilities, security issues, code quality or maintainability concerns. In addition, 5% of components failed a basic integrity check, with 3% having no known origin.

    Open source software is not created equal no matter what consortium is overseeing its development, noted Hasan. Unfortunately, there is no universally accepted method for validating the integrity of open source software, so application development teams should proceed with caution whenever employing open source software, he said.

    The challenge, of course, is that developers have been reusing open source software within applications for years now. It’s only in the recent wake of breaches involving open source software that organizations have been taking a harder look at how applications are developed.

    The Open Source Security Foundation (OpenSSF), an arm of the Linux Foundation, is now at the forefront of an effort to better secure open source software by focusing on 10 streams of investment that, in total, will require more than $150 million in funding to drive greater adoption of DevSecOps best practices among maintainers of open source software projects. The issue is that many of those projects are maintained by a small number of programmers that voluntarily contribute their time and effort to build components that others are free to use. Like any other developer, the amount of security expertise those individuals have is limited. The onus for making sure open source software is secure when deployed in a production environment belongs to the organization that deploys it.

    Hopefully, following a recent executive order issued by the Biden administration, the level of open source security will steadily improve in the months ahead—assuming, of course, the organizations that rely heavily on open source software make more substantive contributions to securing it. In the meantime, organizations can count on the fact that cybercriminals have taken note of the vulnerabilities in applications that can be traced back to flaws in a wide range of open source software.

  • Mobb Launches Community Edition of Automated Remediation Tool

    Mobb Launches Community Edition of Automated Remediation Tool

    Mobb today made available a free community edition of a namesake tool that creates fixes to open source vulnerabilities. The fixes are based on the results of code scanning by a static application security testing (SAST) tool.

    Fresh from raising $5.4 million in seed funding, Mobb CEO Eitan Worcel said the company developed a tool that creates validated patches based on the scan results of third-party SAST tools from Checkmarx, GitHub and Snyk. Initially, the tool is aimed at Java vulnerabilities.

    Worcel said that most vulnerabilities can be traced back to eight types of mistakes that developers routinely make. The company created a pattern for those common mistakes, along with patterns to fix them, called Mobb rules. That ability enables Mobb to accurately produce a code fix that both remediates the vulnerability and adheres to the correctness of the language to eliminate code defects. Mobb then makes available a patch to address those issues in a way that can be readily downloaded and applied by developers. Mobb does not automatically apply those fixes because most developers prefer to review them first, noted Worcel.

    The Mobb approach eliminates the need for IT teams to sort through SAST scans themselves, which can result in the discovery of thousands of vulnerabilities that might theoretically need to be fixed. Each vulnerability, at minimum, is going to require 30 minutes to fix, so the Mobb platform provides a better return on investment and reduces patch backlogs, added Worcel.

    For decades, application development teams have been attempting to find a way to remediate vulnerabilities as quickly as possible. With increased focus on adopting DevSecOps best practices to address those vulnerabilities, more organizations are now reviewing which types of vulnerabilities should have a higher remediation priority.

    Historically, one of the reasons for the wide divide between application development teams and cybersecurity professionals is that many of the vulnerabilities being discovered don’t actually impact applications running in production environments. Development teams then find themselves wasting time investigating vulnerabilities and, when they do determine a vulnerability is an issue, spending time developing a patch. The Mobb tool addresses that later issue by automatically creating the necessary patch, said Worcel.

    Many organizations just allocate junior developers to the task of remediating vulnerabilities, which Worcel noted frees up more experienced developers to focus on writing additional business logic.

    There will, of course, be vulnerabilities that the Mobb tool will not be able to address simply because there may not be enough affected applications to warrant pre-building a patch. Even with the most robust set of rules, it is improbable that an automated tool can, with 100% certainty, safely fix every issue. However, given the number of routine vulnerabilities present in applications, such as SQL injections, there’s plenty of opportunity to improve developer productivity by relying on patches that have already been developed.

    One way or another, the process by which patches are prioritized, developed and then applied has long been a source of DevOps frustration. Anything that streamlines that process will go a long way toward improving the overall state of application security at a time when cybercriminals are becoming more adept at exploiting vulnerabilities wherever they may be found.

  • Endor Labs Taps ChatGPT to Identify Secure Open Source Software

    Endor Labs Taps ChatGPT to Identify Secure Open Source Software

    Endor Labs has launched DroidGPT, an extension of its software for assessing risks in open source code. DroidGPT integrates the ChatGPT generative artificial intelligence (AI) platform to make it simpler to discover the most secure version of an open source package.

    That capability makes it possible for developers to launch a natural language query from within the Endor Labs platform that asks ChatGPT to identify, for example, the most secure logging modules of Java applications.

    Endor Labs CEO Varun Badhwar said the goal is to make it simpler to apply guardrails to application development processes that today rely heavily on reusing open source packages. Developers often wind up using an older version of those packages that are not as secure because a known vulnerability has not been remediated.

    Endor Labs’ Dependency Lifecycle Management platform applies graph analysis to identify the depth of dependencies that exist within an application. That capability makes it simpler to identify where vulnerable components have actually been employed within an application. DroidGPT extends that capability to identify the most secure versions of those components that developers should be using, said Badhwar.

    Having a full understanding of their dependency graph also lets customers generate and analyze accurate software bills of materials (SBOMs) as applications are dynamically updated, noted Badhwar.

    The level of dependency on open source software packages to create applications has risen sharply over the years. An analysis of nearly 2,000 software packages published by Endor Labs found 95% of all application vulnerabilities can be traced back to a transitive dependency created when a developer used an open source component. Functional dependencies are created whenever developers download a third-party component, so it is crucial to assess the risk levels created by those dependencies.

    Fortunately, in the wake of a series of high-profile breaches, there has been increased focus on securing software supply chains. The ongoing challenge is most developers don’t have a lot of cybersecurity expertise, so as contributions are made to open source projects it’s relatively easy for mistakes to be made. The simple truth is many applications deployed in production environments are riddled with known vulnerabilities that have yet to be addressed. DroidGPT is designed to make it simpler to begin the process of remediating software vulnerabilities in applications as they are built and after they are deployed.

    There is no doubt that it will take years before organizations are able to implement a set of truly mature DevSecOps best practices to teach developers how to build more secure applications. That journey, however, needs to begin with tools that enable them to address fundamental issues that dependencies inevitably create. After all, the best way to combat application vulnerabilities is to make sure they don’t manifest themselves in code in the first place. Making it easier to update applications using older versions of open source modules is nothing less than critical at a time when cybercriminals are getting more adept at exploiting them.

  • PagerDuty Bets on AIOps and Automation to Simplify IT Management

    PagerDuty Bets on AIOps and Automation to Simplify IT Management

    PagerDuty this week made generally available an artificial intelligence for IT operations (AIOps) platform that leverages the data model embedded in its incident management software to reduce the amount of time required for an AI platform to learn how an IT environment operates.

    Jonathan Rende, senior vice president and general manager for PagerDuty, said that approach enables the PagerDuty AIOps platform to begin surfacing valid recommendations sooner than other AIOps platforms. Other platforms first need to be integrated into IT environments by an expensive team of consultants and integrators, he added. The PagerDuty approach makes the benefits of AIOps immediately available to both IT operations teams and application development teams alike, he said.

    The arrival of PagerDuty AIOps comes on the heels of the launch of PagerDuty Process Automation, a platform for orchestrating automation across multiple platforms that is integrated within PagerDuty Operations Cloud. That capability makes it possible for organizations to centralize the management of islands of automation that have emerged across the enterprise without having to replace any framework that has already been deployed, said Rende. There are also more than 700 integrations between PagerDuty AIOps and the PagerDuty Operations Cloud platform.

    Like other platforms, PagerDuty AIOps relies on machine learning algorithms and an AI model created by PagerDuty to correlate events so that the platform can put them in context. Incident response recommendations are then made, which an IT team can accept and automatically implement or reject. The platform then uses those inputs to surface more accurate recommendations over time, said Rende.

    The platform will also automatically group apply defined rules coupled with its machine learning capabilities to aggregate alerts. That capability reduces the overall alert noise that might otherwise be generated, noted Rende.

    Organizations are looking to invest in AI and automation more heavily at a time when many of them are reducing the overall size of their IT staff, said Rende. The challenge with AI, however, is that it can take six months or more for a rival AIOps platform to learn enough about an IT environment to begin making recommendations. The PagerDuty approach takes advantage of the data within the PagerDuty incident management platform to train an AI model more quickly, in a way that reduces the time to generate valid recommendations to a matter of days, noted Rende.

    PagerDuty has been building that AI model for the last four and a half years with that specific goal in mind, he added.

    It’s not clear how quickly organizations are embracing AIOps, but as IT environments continue to evolve, many once-manual tasks are being automated. There is a natural fear of the impact those advances might have on specific roles within an IT organization, but as IT environments get more complex, there will always be a need for IT personnel that can focus on higher-order tasks, said Rende.

    Overall, the rate of IT staff turnover should also decline as much of the toil that contributes to burnout is also eliminated.

    It may be a while yet before AI becomes pervasively applied to manage IT, but the proverbial genie is not going back into the bottle. The challenge is determining precisely where the line between the man-machine interface with IT environments is today versus where it might be tomorrow.

  • Pulumi Brings Advanced Analytics and AI to IaC Platform

    Pulumi Brings Advanced Analytics and AI to IaC Platform

    Pulumi today launched a platform that leverages analytics and artificial intelligence (AI) to generate infrastructure-as-code (IaC) from natural language prompts in addition to automating infrastructure management.

    Pulumi CEO Joe Duffy said Pulumi Insights will substantially improve the productivity of DevOps teams in addition to ultimately reducing the total cost of cloud computing.

    At the core of Pulumi Insights is an analytics engine automation application programming interface (API) that enables DevOps teams to launch queries using a search interface or natural language prompts across more than 100 cloud computing platforms. Results can then be organized to highlight the most-used clouds and resources broken down by project and environment using built-in dashboards. There’s also a REST API that makes it possible to export that data to a data warehouse such as Snowflake, Amazon Redshift, Google BigQuery and Azure Synapse.

    In addition, Pulumi has created a companion website and command line tool to make it possible to create code for provisioning infrastructure. That uses large language models like those at the core of generative AI platforms such as GitHub CoPilot or ChatGPT that promise to eliminate much of the toil associated with writing code.

    Pulumi has been making a case for an alternative approach to IaC tools, such as Terraform, that gives enterprise IT organizations more control over how IT environments are provisioned. Too many developers with limited cybersecurity expertise are provisioning infrastructure themselves, resulting in misconfigurations that cybercriminals can then easily exploit.

    The Pulumi approach also makes it simpler for IT teams to create platform engineering teams that can centrally apply guardrails that prevent those issues from arising without slowing down the rate at which applications are being built and deployed, noted Duffy.

    There’s also a much greater focus on improving the productivity of DevOps teams during uncertain economic times as organizations look to either limit or reduce the number of full-time employees they hire or retain.

    For one reason or another, DevOps teams are going to be required to find ways to become more productive as the number of applications being built and deployed continues to increase. AI clearly has a major role to play in enabling organizations to achieve that goal. However, before organizations can reliably benefit from those advancements, they will need to ensure the accuracy of the data those AI models use to surface recommendations and generate code. Otherwise, no matter how advanced these platforms are, the results will be suboptimal.

    In the meantime, IT organizations will need to revisit how IaC tools are being employed in an era where the focus on securing software supply chains has dramatically increased after some high-profile breaches. The challenge is that, in addition to all the applications currently being developed, there are thousands more that were deployed by developers without the same emphasis on cybersecurity.