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Category: DevOps Toolbox

  • Embedded Connectivity Solves the Right (and Left) Problem

    Embedded Connectivity Solves the Right (and Left) Problem

    In Steven Bellovin’s book, “Thinking Security: Stopping Next Year’s Hackers,” (2015) he wrote:

    “Companies are spending a great deal on security, but we read of massive computer-related attacks. Clearly, something is wrong.

    The root of the problem is two-fold: We’re protecting (and spending money on protecting) the wrong things, and we’re hurting productivity in the process.

    InfoSec is hurting productivity in the organization it is supposed to be protecting.”

    Bellovin clarified what he meant by “the wrong things.” He observed that security measures tend to stick around even when the threat they were intended to address has faded into the past. Those security costs have only increased since 2015 when the book was published, and today represent a trillion-dollar drag on the global economy despite the emergence of security-integrated models of software delivery such as DevSecOps, shift left and shift right.

    The 2021 IBM Security Threat Intelligence Index reports the No. 1 attack vector was a surveillance technique known as scan-and-exploit. Defenders face an asymmetric engagement with such network threats. The successful attacker needs only to exploit a single vulnerability whereas defenders must secure 100% of the network’s ports of entry 100% of the time. The fundamental problem is the anonymous and permissionless nature of the internet. This makes it both useful and easily abused. Anyone anywhere may conduct reconnaissance and launch attacks at a massive scale with little cost or risk. How can we make it prohibitively difficult or costly to successfully surveil and attack our own applications? What if we could reliably defend against the entire category of active scanning tactics?

    There are no secure networks, only isolated networks. Taking this back to first principles, a network’s purpose is to convey packets of data from sender address to recipient address. The network has no built-in ability to identify either party or to discern the legitimacy of any activity. Active scanning techniques identify potential targets that are then probed by scripts that may be intrusive or stealthy or both. We could simply reject all incoming network traffic with a firewall, but application servers must be continually reachable in order to listen for incoming requests. Any interruption of availability causes errors like “connection refused” and “service unavailable” for the user. 

    This necessary network exposure leaves the upper layers of the network and the application server itself vulnerable to intrusion and denial of service attacks. “Private” enterprise applications are similarly exposed to the famously-vulnerable group that are VPN users’ devices, and so are by no means immune to this problem. This presents the problem of automatically discerning which incoming connections are legitimate, continually, forever. Are we trying to solve the wrong problem?

    Ideally, we would identify the sender before they connect to the application server. That way, we can know whether they have permission to connect at all. This is something we simply can not do when the sender is known only by a network address. We can’t ask for a credential because that introduces yet another application layer with all of the same problems. This is precisely why VPNs do not go far enough: A superficial barrier is erected for users that prevents the application server from checking for permission until after surveillance and subsequent attacks have occurred. 

    Embedded connectivity-as-code solves the right problem. This represents a shift in thinking about the relationship between the application and the network. Now, the application brings its own point-to-point overlay with a verifiable identity on both ends. This has significant implications for the security of the application that can be summarized in terms of shifting security left in the development timeline and shifted right in the life span after deployment.  

    Shift left: OpenZiti makes embedding connectivity-as-code convenient for developers and transparent for auditors and users. As open source software, it fits into DevSecOps processes that scan the source code. The management side of the OpenZiti network is cloud-native and API-driven and fits into the DevOps paradigm of continuous integration testing. For example, a network-as-code may be continually created and destroyed by a pipeline to exercise any imaginable configuration of endpoints, services and policies, or to facilitate dynamic scanning of the data edge and fabric APIs. 

    Shift right: OpenZiti allows us to shift right security to measure and monitor applications after development, in production, with a novel ease and without compromising security. Operators maintain perfect visibility of deployed applications and need not cope with the complexity of hopping through security gateways or bastions or worrying about external malicious actors and inbound connections. This best-of-both-worlds reality is a natural effect of  an access control scheme that is based upon verifiable cryptographic identity, not a weak identifier like an IP address. Importantly, this means it is always knowable which identity is accessing what services, when they were accessed and to what extent.

    OpenZiti gives applications superpowers! To name a few: Identity-driven addressability, private domain name system, shelter from unauthorized connections, portable and programmable micro-segmentation, end-to-end encryption, solves the IPv4 shortage without IPv6, smart routing to choose optimal routes across the internet and is transparent to end users.

    Learn more about OpenZiti by checking out our Quickstarts or engaging in conversation on Discourse. 

  • AI: It’s What Staff Crave!

    AI: It’s What Staff Crave!

    If you’ve seen Idiocracy (Side note: I am not recommending the movie; I’m using a phrase from it we’ve all heard. The movie itself was a comedy not at all meant to be family-friendly when it was made, and changes in social mores since its 2006 release make it far less acceptable today), you are familiar with the title phrase I’ve used here. That’s what is repeated almost religiously to justify why they buy/use/drink Brawndo, an energy drink.

    One of the scenes involves people telling the star that plants need Brawndo—“It’s got electrolytes … That’s what plants crave!” This is repeated over and over until he asks, “What are electrolytes?” After some stuttering and failures to answer, someone says, “It’s what they make Brawndo out of.” Someone else inevitably tacks on, “It’s what plants crave.”

    Now, substitute “AI” for “Electrolytes”. That’s where we seem to be. Ask a vendor what algorithms they’re using, where their training data comes from, the difference in results between the test reservations and the real world. Most will end up saying a lot, not answering your questions and ending with, “It’s what staff crave!” or some such.

    A few companies will lay it all out there, assuming you can parse through the information and are familiar with the AI training algorithms they are using or the algorithms they’re using to classify inputs against the dataset. And these companies are grand because they (a) know what they’re actually doing beyond “It’s got AI/ML, it’s what staff crave,” and (b) Expect that you are knowledgeable about the data you’ve asked for or are capable of looking it up in this age of wonder that has boundless information at your fingertips.

    Don’t let your vendors get away with just repeating “It’s got electrolytes” over and over. AI and ML are a massive help in some areas, but they’re not yet a panacea and success rates vary wildly whether we’re talking problem domains, datasets, algorithms, training style, etc. Make them tell you the details. Where the data comes from and how big that dataset is. How much was reserved for testing and whether it was reserved randomly. Ask them what they hope AI will achieve, then ask them how that correlates to human interaction. Most AI today is still evaluating or replicating human interactions with something—most AI in IT particularly so. What algorithms were used to validate? And finally, make sure to ask for real-world results. I know people in data science and one has confidently said to me, “My models are 95% accurate.” When I finally dug into that claim (because my experience is that real-world use of AI/ML is almost never that accurate) it turned out that claim was true … for the reserved test data. So, the same dataset used to train got the same results on the reserved subset. That’s a good thing, but it is no indication of how said algorithm will do in the wild with unfiltered data. The big problem really is the switch from test data to real-world data.

    In fact, in IT, we are a little better off than most spaces wrestling with AI/ML, because very often there is no difference between test and training data in IT. A network security vendor will test against actual traffic—because they have access to massive volumes of it and it shortens the iterations to use what’s at hand and what the system is being trained for.

    And keep rocking it. The current iteration of IT is the opposite of the dark comedy future depicted in Idiocracy. You all are putting up systems and keeping them running at increasingly fast cadences; sometimes remotely because of COVID-19 or other work-related issues. Don’t stop turning out success for your coworkers and customers and take a moment to force vendors to explain what they’re doing with AI. If you’re a generalist like me, ask over email, though, so you can go look up the answers and make sure you understand the details.

  • Survey Predicts Massive Migration to the Cloud

    Survey Predicts Massive Migration to the Cloud

    A global survey of 1,600 enterprise IT decision-makers found more than half are working for organizations that plan to eliminate the use of data centers over the next two years.

    The survey was conducted by Aryaka, a provider of a secure access service edge (SASE) platform, and found a quarter of the respondents have closed 25% to 50% of their office sites; 75% of respondents said at least a quarter of their employees will remain remote permanently post-pandemic. Just under a third (32%) expect at least half of their workforce would be permanently remote.

    Despite the apparent accelerated shift to the cloud, the report found a full three-quarters of respondents (75%) are projecting at least a 10% growth in IT spending, with a quarter expecting IT budgets to grow 25% or more. Nevertheless, more than a third (36%) report they have IT cost concerns.

    David Ginsburg, vice president of product and solutions for Aryaka, said the shift to the cloud in the wake of the COVID-19 pandemic is being driven mainly by digital business transformation initiatives that require applications be quickly built and managed by IT teams that are working from home themselves. With the pandemic entering the third year, a hybrid workforce that regularly shifts between home and the office is now permanent, he noted.

    Consequently, with more people working from home, nearly two-thirds of respondents (64%) said they will shift toward a SASE-based approach over the course of the coming year to provide secure access to cloud applications over a software-defined wide area network (SD-WAN). Over two-thirds of those respondents said they will opt for a managed SASE offering rather than build and maintain these platforms themselves.

    SASE was originally coined by Gartner to describe a cloud-delivered service that brought together networking and security offerings. Those offerings should include a firewall-as-a-service (FWaaS), secure web gateway (SWG), cloud access security broker (CASB) and zero-trust network access (ZTNA). In effect, it’s a secure software-defined wide-area network (SD-WAN). As organizations adopt SASE-based platforms and services, just under a third of respondents (61%) are recognizing that dynamic bandwidth reallocation will be a very important requirement as workers shift between the office and home.

    The transition to SASE involves setting a strategy (35%), phasing out of legacy virtual private networks (32%) and consolidating cloud security with zero-trust (29%). Top desired capabilities include SWG (47%), SD-WAN (36%) and FWaaS (28%). A full 41% reported that responsibility for networking and security decisions have now been consolidated into a single team. However, only slightly more than a third (34%) said security is a major priority.

    The implications of a shift to the cloud at this rate and level of scale are likely to have a profound impact on DevOps teams. While cloud infrastructure is designed to scale on demand, accessing applications across an SD-WAN can have a significant impact on application performance. The survey, however, finds only 29% of respondents are concerned about whether applications will perform properly. Nevertheless, well over two-thirds (69%) said there will be an increased focus on observability and control, while 42% noted identifying slow performance for remote and mobile users will be a key issue and 37% expected slow performance at the branch office to also be an issue. Lackluster application performance is a time sink, according to 42% of respondents.

    Naturally, many IT teams will add new tools and platforms to deploy and manage cloud applications. The Aryaka survey found that only slightly more than a quarter of respondents are concerned about the newness of the technology they might employ.

    The coming year is shaping up to be incredibly disruptive to the IT landscape. The challenge is making sure balance is maintained between cost, flexibility and application performance.

  • Securing Software with Intelligent Pipelines

    Securing Software with Intelligent Pipelines

    One of the biggest cybersecurity risks involves vulnerabilities in the application layer. After all, the best firewall is useless if the web application itself is vulnerable. Many companies have worked to mitigate these risks by investing in their AppSec programs. According to a recent whitepaper written by ESG (commissioned by Synopsys), 71% of companies surveyed now use AppSec tools for more than half of their software projects. Notably, over two-thirds of companies already use 11 or more automated application security testing (AST) tools, such as SAST, DAST, IAST, fuzz testing and container scanning solutions.

    This is due, at least in part, to the fact that tool manufacturers have now made their products “DevOps-ready” and support suitable integrations with CI/CD pipelines. This makes it tempting to simply let AppSec scanners run in the pipelines, but that can introduce other problems.

    Problems with AppSec in CI/CD Pipelines

    Too many results: Developers can be inundated with findings, yet only a small percentage are likely to pose such a high risk that they need to be fixed immediately. But the prioritization guidelines are often formulated in separate documents and are ambiguous.

    Development pipelines are slowed down: Build pipelines often run at frequent intervals; every second to every minute. Scans with AppSec tools may take several minutes or even hours.

    Manual AppSec activities are left out: Not all AppSec activities can be automated, such as architecture risk analyses, threat models and penetration tests. Nevertheless, these are an essential part of the AppSec strategy.

    Intelligent pipelines (i.e., intelligent, purpose-optimized automation and orchestration of the various AppSec tools and activities) are ideal for overcoming this challenge. Combined with the consolidation of scan results, a new category of solutions has emerged here, which Gartner dubbed application security orchestration and correlation, or ASOC for short, in 2019.

    How Pipelines Become Intelligent

    The “intelligence” lies in deciding which tools need to run at what time and what to do based on the results. So instead of scanning the entire codebase with AppSec tools at every commit, it dynamically decides which scanner needs to run and to what extent. This decision can take into account various parameters such as the scope of the actual code change, the risk profile of the application or the development stage of the software.

    The risk profile of the application should also be considered. Web applications that are accessible from the internet and process sensitive data pose a greater security risk than an internal tool for generating documentation. Such risk profiles usually emerge from prior architectural risk analyses and threat models.

    Furthermore, the scope of AppSec testing should be appropriate to the development stage of the application. Individual commits of a feature branch should be checked mainly by static code analysis for passwords and API tokens contained in the code and compliance with coding guidelines, such as SEI CERT, to support rapid development. Later on, during the merge request into the main branch, more extensive scans should be added, including deeper data flow analyses, which then detect cross-site scripting or SQL injection attacks. A longer runtime can be accepted here since such merge requests usually have to be approved according to the dual control principle.

    Code Security Policies

    The core of intelligent pipelines lies in individual guidelines or policies. These define when specific AppSec activities are executed. Additionally, these policies describe how to proceed with the combined results (e.g., whether the code may be integrated into the master branch or the web application may go live).

    The policies are described in a configuration file; this is policy–as-code. Just as with other as-code methods, this enables or improves uniqueness, reproducibility and automation. Simple policies can be created according to the “If this, then that” principle. For example, a software composition analysis (SCA) scan can be triggered when either the project file changes (package.json, go.mod, pom.xml, etc.) or new files or directories are added, but not when only existing source code files are changed.

    Conclusion

    Whether with or without orchestration tools, anyone who wants to make their software more secure must inevitably consider which AppSec activities make sense at what time. In other words: The right scan at the right time. Furthermore, it should be determined how to proceed with the results before ordering or even automating a scan. To do this, you need to understand what the greatest risks are and how to protect the software from those risks.

    Intelligent pipelines help to implement and, above all, automate an AppSec strategy. A corresponding strategy is a prerequisite. But even (or especially) without a clear strategy, it makes sense to look at the architecture of an intelligent pipeline, as this both encourages collaboration between development, AppSec and DevOps teams and raises the right questions that lead to a successful AppSec strategy.

  • When to Use API Management and Service Mesh Together

    When to Use API Management and Service Mesh Together

    Though sometimes described as competing architectures, API management and service mesh have slightly different use cases and can actually work well together. Whereas API management provides the business logic for outside-facing traffic, a service mesh excels at handling intercommunication between microservices. An organization could certainly adopt both simultaneously across their projects. So, when does it make sense to use both API management and service mesh?

    I recently chatted with Mark Cheshire, director of product, Red Hat, to discuss the nuances between API management and service mesh. According to Cheshire, API management and service mesh can work quite well side-by-side for particular use cases. For example, a large organization using service mesh could benefit from applying API management that wraps microservices in a usable contract for internal departments. Or, API management could help a company expose specific APIs from the mesh to outside partners.

    When to Combine API Management with Service Mesh

    Say you already have service mesh—do you need API management, as well? Cheshire offered four questions to keep in mind that may influence your decision.

    1. Consider the number of connections and the number of consumers: Is it many-to-one or one-to-many?

    Within a monolithic application, communication is more centralized—there is more of a one-to-one relationship. However, when you break apart services into microservices and reuse them across an organization, a service may now have many consumers. This is a good indication you need an API management approach for the interface.

    “When environments get larger and larger, you can’t manage that as a flat mesh,” explained Cheshire. “You need some kind of organizational grouping by line-of-business or by the functions that you may have.”

    API management can create more formal interfaces between internal groups in a large organization that likely have little to no contact with one another. For example, perhaps the customer data team needs a formal method to connect with the sales organization. Or, maybe a security team supporting a login microservice needs to access financial details from an accounting division.

    2. What sort of access control and limiting does the system require?

    Consider what access control is necessary to secure your environment and avoid service disruption. A service mesh will typically use a zero-trust approach, applying mutual TLS over every ingress to each microservice. API management can also bring authentication controls and more advanced authorization and traffic mechanics.

    API management can provide a streamlined way to apply fine-grained controls for inter-domain traffic that follows the business logic. This helps assign access to specific resources to different requesting parties, such as read-only access for basic users or update access for admins. Amid increasing API attacks, setting fine-grained policies and permissions around your endpoints is becoming increasingly important to avoid data overexposure. This imperative increases as the binary between external and internal traffic fades with more public cloud use, remote work and third-party contractors.

    API gateways also place greater emphasis on communication rules and patterns. Rate limiting, for example, is very important to avoid collapsing entire networks, explained Cheshire. In addition to rate limiting, filtering, pagination and content negotiation are also standard API management capabilities that further optimize the exchange between the client and server.

    3. Do you require documentation for the services?

    In a monolithic setup, you likely have all developers sharing the same repository, explained Cheshire. This may still be true within an internal network of distributed microservices wherein each microservice is independent, yet developers can still access each other’s repositories. When you’re connecting services inside a domain, it’s often just the code itself (and perhaps some in-line commenting) that represents the contract between services.

    However, north-south traffic requires much more formalization, he said. Formal contracts are often necessary when one domain is communicating with another within the same company. In this scenario, there’s less tribal knowledge around how things work. API management is useful, in this case, to produce a developer portal with human-readable documentation that explains the methods, request types, URL structures and sample calls. This makes it much easier for someone without preexisting knowledge of the system to integrate with it.

    This formal contract is often generated from a specific file, such as OpenAPI Specification, which has emerged as industry-standard metadata for describing REST APIs. Groups can use OpenAPI to automatically generate documentation and even drive specification-first development. All in all, standardizing the way you document APIs and microservices can significantly improve the knowledge-sharing around how services connect.

    4. What is the nature of interactions?

    Another trait to keep in mind is the nature of interactions between services, Cheshire said. He divided systems into two large types: First is a graph model, in which any service can connect to any service. The second is a hierarchical model in which north-south traffic enables one service to have multiple consumers. A hierarchical model is necessary for productization and SaaS since it creates a single intercept point to the underlying resources. Maintaining hierarchy here is vital to retain simplicity, as you wouldn’t want the consumer to be able to hit all private microservices in a connected graph (unless that’s the intent!).

    API management thus enables the sharing of APIs on the mesh. “While they both use communication proxies, and while there are parallels in function, the differences in domain set them apart, and most companies will see significant benefits from using them together,” wrote Dino Chiesa and Greg Kuelgen on the Google Cloud blog.

    Better Together

    In summary, API management and service mesh go well together. “The two are complementary and can work together,” added William Morgan, CEO, Buoyant. Joaquim Moreno wrote, “they are not mutually exclusive; we should think about them as different layers in your infrastructure.”

    Specifically, combining the two is beneficial when:

    • Exposing and productizing APIs on the mesh
    • Formalizing inter-service communication in large corporate networks
    • Applying fine-grained access control; rate limiting for API-based microservices
    • Standardizing services with more formal documentation
    • Interaction is a more hierarchical communication model
    • Providing access to partners
    • When you need to externalize data to third-party plugins or integrations

    “The worlds of APIs and services mesh are becoming ever more closely connected,” said Cheshire. And API management won’t stop at REST—as developers adopt new integration styles like Kafka, GraphQL and asynchronous formats, the solutions will evolve. “It’s essentially a governance layer for all your integration points.”

    Large companies that seek to standardize inter-domain traffic within organizations will likely benefit from API management. But API management also offers the potential to productize internal infrastructure. Another benefit is that an API management gateway may also monitor and log requests, helping spot actors at the perimeter and keeping a helpful tally of incidents.

    Of course, adopting both API management and service mesh may be overkill for some scenarios. And, it’s good to remember that introducing additional layers inherently increases overall latency. It’s up to developers and architects to weigh the options and determine what’s appropriate for their unique environment. To enable this, a healthy culture of open communication is important to ensure teams are on a similar page across divisions, to avoid mismatched adoptions that only increase technical debt.

  • Harness CD Platform Now Available via Source-Available License

    Harness CD Platform Now Available via Source-Available License

    Harness today announced a free community edition of its continuous delivery (CD) platform is now available under a source-available license rather than a more traditional open source license.

    Scott Sanchez, chief marketing officer for Harness, said the goal is to make CD as accessible to developers and DevOps teams as their current CI platform. Harness already makes an open source edition of its continuous integration (CI) platform available.

    The Harness CD platform is available under a Polyform Shield license that provides free access to the platform, including the right to use, modify and distribute it but not launch a competitive offering. A source-available license is an alternative to an open source license that allows an organization to potentially create a fork of the Harness CD that could, for example, be delivered as a cloud service. That specific issue has led to scenarios where vendors that create an open source project find themselves competing for revenue against a cloud service provider that is not necessarily as committed to contributing and maintaining the primary open source project. At this juncture, it’s not clear how many other vendors that make software available under an open source license might take a similar tack.

    While most organizations that embraced DevOps have been able to successfully implement a set of CI best practices, the number of organizations that have implemented CD is not nearly as high. One reason for that is automating deployments across platforms has never been especially easy. Each platform tends to be customized to the point where it is difficult to automate application deployments. In effect, each platform is its own unique type of “snowflake.”

    However, as the number of digital business transformation initiatives continues to steadily increase, the need to regularly update application experiences is going to require more organizations to implement a set of CD best practices. A source-available license should help increase the number of DevOps teams that can access and experiment with a CD platform, noted Sanchez. The challenge, of course, is that many of those applications are now based on microservices with components widely distributed across an IT environment.

    Historically, CI and CD have been synonymous when viewed within the context of a CI/CD platform. It’s not clear to what degree DevOps teams will look to better leverage the CI/CD platforms they already have in place rather than deploying a dedicated CD platform for IT operations teams and letting developers choose whatever CI platform they prefer. In general, a recent survey conducted by the Continuous Delivery Foundation found that 80% of respondents that work for companies with two or more employees are involved in DevOps to some degree.

    Regardless of approach, the coming year is shaping up to be one where many more organizations take DevOps initiatives to the next level. Arguably, as more organizations realize how dependent they are on software to differentiate themselves from rivals, the ability to deliver new application experiences has become critical. At the core of those efforts is a set of DevOps best practices that will ultimately determine just how competitive those organizations will be.

  • Testlio Adds Fused Testing to Automate Testing Platform

    Testlio Adds Fused Testing to Automate Testing Platform

    Testlio announced today it is adding fused testing capability to its application testing platform; Testlio is used by IT organizations to manage application testing teams consisting of internal employees and external contractors vetted by Testlio.

    Fused testing describes the combination of automated and manual testing IT organizations use to improve overall productivity. Fused testing strikes a balance between tests that can be automated and those that still need to be performed by a human.

    To achieve that goal, Testlio has partnered with Applitools, BrowserStack, Headspin, Mabl and Sauce Labs. Failure signals and session artifacts from these tools can be pushed to the Testlio platform to streamline the triage of test results. Testlio is making six device slots and 100 hours per month of automated testing processing time available at no additional cost.

    Finally, Testlio has set up an automated testing center of excellence (CoE) as part of an expansion of its professional consulting services it provides to help organizations implement testing best practices.

    Testlio CEO Steve Semelsberger said those integrations will make it simpler to augment human testers and increase the rate at which applications can be tested as many organizations are accelerating the rate at which those applications are being built and deployed.

    As organizations seek to implement continuous testing within the context of a DevOps workflow it’s apparent there will need to be more reliance on automation to keep pace with application development, he added.

    Through its platform, Testlio provides organizations with access to more than 10,000 freelance application testers on demand. That network of testing professionals can create, run, optimize and diagnose automated tests using Appium and Selenium frameworks written in programming languages such as Python, Java and Node.js. Only 3% of the freelance application testers that apply to join the Testlio network have been accepted, noted Semelsberger.

    The degree to which organizations prefer to rely on internal employees versus contractors to test applications will naturally vary. However, it’s difficult to predict what volume of tests will need to be performed—and when they should be performed—to preserve application development timetables. Contractors provide IT organizations with a degree of testing flexibility that is becoming an absolute requirement given the current challenges of hiring and retaining professional application testers, said Semelsberger.

    Application testing is, of course, difficult to apply uniformly no matter how many testers are available. In addition to providing access to experienced freelance testers, Testlio also provides a platform for managing the overall testing process. The challenge is then aligning those testing tasks with the various application development projects the organization has launched.

    Regardless of the approach to testing, the need for it has never been more critical. As organizations embrace a wide range of digital business transformation initiatives, a much higher percentage of the applications are now externally facing. The tolerance for suboptimal application experiences among customers and partners is much lower than that of internal employees. As such, first impressions, like it or not, are going to determine the success and failure of most of those application initiatives.

  • Low-Code SaaS Solutions Need Security at Their Core

    Low-Code SaaS Solutions Need Security at Their Core

    Faster integrations. Accelerated software delivery. Elevated user experiences. These are only three of the benefits organizations gain by incorporating low-code platforms, apps and solutions into software development and business process management.

    Low-code platforms aren’t new, but demand for the technology soared in the last two years as companies were challenged to meet stakeholder demand for more digital transformation. With low-code tools, companies find they can expand digital transformation efforts through faster business application delivery. In addition, the time it takes to innovate is dramatically reduced with low-code tools.

    Low-code solutions are no passing trend. In their recent report on global cloud revenue, Gartner predicted that by 2025, the use of low-code and no-code will “nearly triple,” adding that “70% of new applications developed by organizations will use low-code or no-code technologies, up from less than 25% in 2020.”

    Low-code solutions give organizations the ability to produce working solutions and integrations with more speed and agility than more traditional on-premises development. Integration used to be a labor-intensive IT process requiring custom development on both sides of the integration. As more organizations adopt a “buy versus build” approach to software development, they are turning to self-service and other solutions with “out of the box” functionality to increase speed and efficiency. In addition, the vendor-developed components of low-code tools also offer a better user experience.

    Rising Demand for Low-Code, Platform-based Solutions

    As organizations expand their product and service offerings, the demand for low-code, platform-based solutions will increase. SaaS and PaaS solutions with a low-code focus can be effective for organizations looking for accelerated functionality. Often these tools include pre-built models and templates that businesses can quickly implement and easily duplicate for flexibility and versatility.

    The ability to create seamless integrations with other applications is a must. Best-of-breed low-code platforms enable intelligent integration between multiple applications using graphical user interfaces (GUI) as well as industry-standard interfaces such as JSON and APIs within vendor-supplied environments. Intuitive visual user interfaces and drag-and-drop features allow businesses to customize the solution to meet their needs and preferences.

    But along with ease of use and rapid functionality, organizations must look for low-code tools with security built-in.

    Security: A Priority for Low-Code Tools

    Security must be a top priority for organizations using low-code tools in any capacity, from using a low-code platform for application development or using a no-code SaaS solution for business process automation.

    Anytime an organization introduces outside applications or any third-party API integration to an environment, the organization is entirely dependent on the vendor’s security implementation. This is especially true when combining various solutions to create a unified product or service. If the vendor doesn’t take security seriously, other parts of a solution could be exposed to risk because of a weakness in another area.

    Problems can get in either intentionally or unintentionally, as demonstrated with the recent Apache Log4j vulnerability. The bug in this commonly used routine was found to have massive potential for cybersecurity attacks. As CNET cautioned, “The vulnerability in the widely used software could be used by cyberattackers to take over computer servers, potentially putting everything from consumer electronics to government and corporate systems at risk of a cyberattack.”

    Ways to Remedy Security Risks of Low-code Tools

    Organizations can’t make assumptions about the security of every tool, solution or platform they bring into their environment. They must complete their own due diligence for security in the software development pipeline or any time a 3rd party app or tool is introduced.

    One critical step to addressing security is incorporating DevSecOps and shifting security left in the software development pipeline. The Log4j vulnerability put the need for DevSecOps in the spotlight. Taking a shift left approach and implementing security earlier and more often in the DevOps process can help to catch more potential vulnerabilities. It’s critical to introduce DevSecOps early in the development cycle, or organizations risk missing a bug or vulnerability. In the development cycle, security isn’t an antivirus scenario; all code must be scanned for vulnerabilities.

    Organizations also need to employ due diligence when exploring options and solutions to bring into their environments. Look for solutions with security at their core. Before integrating any low-code solution, examine the vendor’s approach to security.

    Some security measures to look for when considering vendors or solutions include:

    • All communications should utilize at least TLS 1.2 (transport layer security) and comply with industry standards for certificates and encryption keys.
    • All hashing should be using current accepted standards with HMAC, they should be enforcing security rules on passwords such as only storing password hashes and requiring 2FA if they interact directly with customer accounts.

    Low-code platforms and tools have the ability to accelerate digital transformation efforts without having to rely on hand-coded, on-premises solutions. The potential for time savings and cost savings is significant, but it’s important for organizations to ensure they’re not compromising security when choosing solutions.

  • How AI Can Improve Software Development

    How AI Can Improve Software Development

    Change is constant and inevitable. While some changes are welcome, others are just a necessary evil. 

    Continuous calls for digitalization combined with a pandemic-prompted emphasis on transforming business operations, force organizations across industries to modernize their legacy systems. The emergence of cloud computing has shrunk organizations’ on-premise IT footprints. However, with 96 of the world’s largest 100 banks, 90% of the world’s largest insurance companies, 23 of the 25 largest retailers in the U.S. and 71% of the Fortune 500 still using mainframe systems, any modernization efforts—resulting in increased risk of downtime or defects—will frequently lead to disastrous consequences. 

    So, while making changes can be expensive, uncertain, and lead to customer loss and/or embarrassment in the marketplace, modern business demands that systems change to continue to deliver value. The problem with mainframe modernization, however, is that today’s code search tools, linters and program analysis tools are deficient when it comes to mitigating the risks associated with maintaining and improving systems. 

    Modernization: A Knowledge Problem

    With 10,000 mainframes in active use globally, the average organization annually spends anywhere from 60% to 80% of its IT budget just on maintenance. But, as experienced and proficient programmers retire or move on, many companies are realizing that the specialized domain knowledge of the industry and institution that those developers used to create and update these complex critical systems is walking out the door with them. 

    As the developers who originally wrote the code have long since moved on, and documentation leaves a lot to be desired, developers new to a system are unable to acquire comprehensive knowledge of the system’s functionality, at a detailed or fast enough level, to become adequately productive and proficient. That lack of speed comes with significant risk and cost to any organization.

    Recognizing the full and true intent of the functionality written in code is not simple. Developers today spend roughly 75% of their time searching through source code, to identify the code representing the functionality that needs to be changed. Complicating matters further, understanding the code is not enough. To effectively and efficiently maintain and support any system, software developers must know precisely what an application actually does—and how altering code in one part of the system impacts it as a whole. But since the code representing a behavior that needs to be changed can be littered throughout the system, developers might think they’re making a simple tweak, when really they may be breaking the entire system (and not even knowing it). The risk of change resulting in unintended consequences is real.

    All of that being said, how can developers improve their efficiency and effectiveness?

    The Shortcomings of Today’s Tools

    With code repositories growing unchecked over time to extraordinary sizes, those responsible for maintaining and preserving a system’s functionality say it’s becoming increasingly challenging to find bugs in code without machine assistance. And with debugging being an arduous, time-intensive endeavor, developers are increasingly turning to code search tools, linters, and static and dynamic analysis tools to analyze millions of lines of code, flag errors and suggest fixes. 

    Whether the tool excels at bug localization, code visualization or “error” detection, entirely too many tools are insufficient when it comes to actually identify the specific lines of code that require attention. Program analysis tools only illustrate code in ways that developers still have to interpret (potentially incorrectly) and draw their own conclusions. While code search tools might speed the rate at which developers can build a mental model of the code, such tools are notorious for false positives, and developers still have to undergo the mentally challenging endeavor of piecing that mental model together to identify the code, and then safely make changes. Worse yet, code completion tools—tools that less-experienced developers disproportionately rely on—can actually propose imprecise changes (one prominent tool offers incorrect code 71% of the time), and that introduces risk.

    The cherry on top of this problematic sundae is that today’s tools don’t have any way to effectively validate the scope and accuracy of a proposed change. The influence of a suggested change can be tough to identify, so testing for comprehensive validation is frequently next to impossible. 

    Ideally, there has to be a better way to mitigate risk while making changes to legacy systems. Enter artificial intelligence. By employing a unique approach to AI, the code repository truly becomes a knowledge repository, enabling many capabilities unrealized before now. 

    A Developer’s AI-Enabled Accessible Knowledge Repository 

    By leveraging AI to automate the identification of the specific lines of code that require attention, developers can simply ask this AI-driven knowledge repository where behaviors are coming from—and quickly identify the code associated with that behavior. This puts AI squarely in the position of intelligence augmentation, which is key to leveraging its capabilities. 

    This novel approach of AI reinterprets what the computation represents and converts it into concepts, therefore “thinking” about the code in the same way humans do. The result is that software developers no longer have to unearth the intent of previous developers encoded in the software to find potential bugs. Even better, developers are able to overcome the inadequacies of automated testing by using AI to validate that they haven’t broken the system before they compile or check in the code. The AI will forward simulate the change and determine whether it’s isolated to the behavior under change. The result is the bounds of the change are confined to the behavior under change so that no unintended consequences arise.

    While AI is not yet advanced enough to safely make changes on its own, it can serve as a knowledge repository with the added capability of directing developers precisely where to make any needed changes. With AI, programmers—even on their very first day on the job — can make any necessary fixes with confidence that they won’t break the entire system. 

    Unlike its code search, code navigation, code completion and program analysis tool counterparts, AI fundamentally empowers developers to safely, efficiently and effectively make any necessary changes. With AI — that is fortified by a knowledge repository that understands source code the same way a human does — developers can truly do more with less. 

  • How Low-Code Enables the Composable Enterprise

    How Low-Code Enables the Composable Enterprise

    Enterprises know that their customers and partners expect a superior personalized experience. After all, customer experience has been a top priority for organizations over the last decade. However, despite all the technological advancements and transformation initiatives, the ability to deliver innovation with speed continues to elude. 

    Without this ability, enterprises constantly play catch-up with rapidly shifting business dynamics and customer expectations.

    Why is Strategic Agility so Elusive?

    Enterprises are unfortunately locked in a situation that Gartner Research identifies as “static application experience”. It is a paradigm of rigid and bloated application architecture, creating inertia that organizations find difficult to navigate around. This technological impedance is one of the primary reasons (besides organizational culture and structural challenges) at the root of organizational sluggishness.

    To adapt to the challenges of the modern world, enterprises need to move from a static application experience to a more dynamic one. This can be achieved through the approach — wrapped around a goal — of a composable enterprise.

    Composable Enterprise is the Future of Applications

    A composable enterprise aims to create an application architecture wherein enterprises can deliver various functions through composition (as against development), by leveraging packaged business capabilities (PBCs). Gartner estimates that by 2023, 30% of new applications will be delivered, priced, and consumed as libraries of packaged business capabilities, up from fewer than 5% in 2020.

    To be fair, this run-up to a composable enterprise is not a fresh-of-the-press revelation. Enterprises have been attempting to move from hardcore coding-based development to a more service-and-composition-oriented architecture over the last couple of decades, albeit only in pockets and not as fast as they would have wished.

    Composable enterprise has become the need of the hour. And this is being driven by the sense of urgency created by multi-faceted disruption across industries, coupled with technological advancements that make it possible for organizations to accomplish it at an enterprise scale. 

    One such advancement comes in the form of low code digital transformation platforms.

    How do Low-code Platforms drive Composable Enterprise?

    According to Forrester Research, low-code development platforms are emerging as a key strategy to accelerate app delivery to support digital business transformation. They have the potential to make software development as much as ten times faster than traditional methods. Low-code platforms use visual composition and a modeling-based approach to speed up application development. 

    A good low-code platform drives the creation of a composable enterprise in two ways, both of which are critical for long term sustainability:

    • By enabling rapid development of modern PBCs that deliver cutting-edge functionality without having to rebuild the organizational core, thereby protecting decades of investments in legacy and core applications 
    • By providing the core capability to create applications through composition and abstraction with a modeling-driven application environment, wherein the organization can create customer-centric business applications that deliver a dynamic omnichannel experience and drive the end-to-end customer journey

    Is There a Catch?

    Low code platforms come in various shapes and sizes, from those supporting the creation of relatively simpler stand-alone applications to those that enable the rapid development of mission-critical enterprise-scale applications with various functional components. Mostly, the latter category forms the core of a composable enterprise.

    To drive a composable enterprise, a low code platform needs to be equipped with a variety of capabilities. It should: 

    • Speed up pro-grade development through modular, modeling-driven, and abstraction-oriented development
    • Support a variety of business and technological needs, such as workflows, content management, straight-through transactions, mobile apps, collaboration, customer self-service, omnichannel customer engagement and automated business rules
    • Offer service-oriented integration that can extend beyond the organizational boundaries
    • Enable rationalization of application portfolio by helping avoid the introduction of multiple point-solutions in the mix with its own ability to develop a variety of applications quickly
    • Prevent shadow IT through central governance, visibility, and guardrails, without hindering innovation
    • Enable agile, continuous integration/continuous delivery (CI/CD) and adherence to quality standards and architectural requirements
    • Enable, even catalyze, change with traceability, thereby ensuring long term sustainability of business applications

    What Next?

    The ability of organizations to accomplish a composable enterprise of the future will depend on a multitude of factors. It is essentially a transformational journey.

    It is important to take the right steps and build in phases, starting from establishing a core low code platform and building peripheral capabilities with time. Picking the right set of applications and establishing early wins go a long way in building momentum towards the long-term goals.

    Identification of the right low code platform that can drive your transformation journey to a composable enterprise is critical for achieving a composable enterprise.