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Category: AI

  • 6 Transformative Mainframe Predictions for 2024

    6 Transformative Mainframe Predictions for 2024

    The success of organizations in today’s digital economy demands speed — the ability to quickly respond to market trends with new applications and services, instant access to critical data, and resolution of issues even before they affect service. As we move into the new year, the adoption and adaptation of emerging technologies on the mainframe will enable enterprises to embrace changing market forces, turning them into a competitive advantage.

    Below are six predictions of how mainframe organizations will leverage new technologies to advance the platform in 2024.

    Prediction: AI/ML Adoption Grows

    The roles of artificial intelligence (AI) and machine learning (ML) and their adoption across the mainframe, from application development to monitoring to data management, will continue to grow.

    The generative AI revolution is real and here to stay. In the coming year, organizations will continue to explore best-use cases for its integration into mainframe management. Development teams will leverage key AI-recommended insights into automated workflows and code generation, improving developer productivity. Database administrators (DBAs) will look to automate data management processes to efficiently handle the demand for the diverse datasets required by generative AI tools, and mainframe operations teams will increase their reliance on AI-driven predictive analytics and root cause analysis.

    As adoption increases and experience is gained, organizations will work past their AI trust issues and begin to harness the power of automation to become a fully autonomous digital enterprise. At the same time, there will be a growing need to govern AIOps and MLOps to ensure the validity and accuracy of ongoing model development.

    Prediction: DevEx Comes to the Forefront of Companies’ Innovation Strategies

    With the long-awaited shift of mainframe workforces from those with decades of experience to a new generation of stewards now in full effect, organizations will find a new imperative to attract and retain top talent by providing developers with familiar tools and development environments.

    Professionals who have recently graduated from college or are moving from other platforms bring with them an expectation of integrated and automated DevOps practices. The move towards enterprise-wide DevOps and continuous integration and continuous deployment (CI/CD) pipelines will continue, with many companies also looking to employ cross-platform tooling. By giving developers the freedom to use their integrated development environment (IDE) of choice, organizations can decrease onboarding time and get new mainframe developers up to speed faster, enabling them to understand code and its function quickly and easily, and creating a seamless, frictionless way to make changes, debug, and deploy.

    Prediction: Database DevOps Bridges the Gap Between DBAs and Developers

    Changing customer demand and the need for agility will accelerate the drive for developers to seamlessly access and manage data sources. The demand for an agile development environment that extends from applications to databases will increase the inclusion of database DevOps in CI/CD pipelines, including version-controlled database schemas and data, automated database provisioning, and database testing as part of the pipeline.

    By embracing agile practices, such as incremental database changes and rolling deployments, organizations will bridge the gap between DBAs and developers and ensure that mainframe databases evolve alongside application code. This cohesive approach will enhance collaboration, reduce conflicts, and expedite application development, positioning mainframes as agile and adaptable components within the rapidly changing IT landscape.

    Prediction: Democratizing Mainframe Data

    The move ― and the need ― to democratize mainframe data, making it available to AI/ML analytics engines, will gain momentum.

    Organizations have matured their data lakes and gained valuable insights from data housed on distributed and cloud platforms, but the efforts needed to extract and transform mainframe data stored on tape and virtual tape libraries (VTL) has left volumes of business-critical data behind. By operationalizing mainframe integration with the cloud, organizations can make it easier and faster to access mainframe data and transform it into open formats, making it accessible to analytics engines and AI/ML applications.

    The ability to realize the hidden potential of data they already store will make cloud-based storage of mainframe data a prerequisite for enterprises looking to digitally transform their businesses and gain competitive advantage.

    Prediction: Object Storage Becomes Critical for Security

    Mainframe-dependent organizations will seek to integrate hybrid cloud object storage to fortify mainframe data protection against escalating cyberthreats and ransomware attacks.

    In 2024, cyberthreats will almost certainly increase in number and sophistication. The European Union’s Digital Operational Resilience Act (DORA) seeks to ensure that organizations remain vigilant and protect their systems and their customers’ data against increasing threats.

    With DORA set to come into effect in January 2025, and further regulations and guidelines on the horizon, managing mainframe data across diverse locations while prioritizing immutable data copies will be imperative. Organizations will pivot towards proactive strategies, aligning their mainframe infrastructure with hybrid cloud solutions to meet evolving compliance standards and shield against the growing sophistication of cyberthreats.

    Prediction: Innovation Takes the Lead

    Organizations will adopt ruthless prioritization, shifting focus from maintaining services and applications to building new ones for the future.

    Having seen firsthand the success of ongoing mainframe modernization efforts, organizations will place a new focus on building skills and specialties that align with future goals and product roadmaps, putting them in a position to capitalize on coming trends and giving them the ability to quickly respond to changing market forces with innovative new products and services.

    As companies mature their approaches to AI on the mainframe, many will turn to building “applied AI” teams tasked with developing proactive solutions to better analyze data and forecast trends. In short, organizations will think logically about how to embrace AI rather than being embraced by it.

    In 2024, mainframe organizations will accelerate the adoption of emerging technologies like AI and the cloud to increase the adaptability, resilience, and efficiency of the platform while situating it for even greater success in the future. The mainframe will continue to be a platform of innovation, integrating with new technologies and practices borrowed from other platforms to further the evolution of a hybrid IT environment that meets and exceeds the demands of the modern digital economy.

    The role of AI, the impact of data governance regulations, the mainframe skills gap, affordability, and interoperability requirements are all brewing a perfect storm around the mainframe that will mark 2024 as a year of significant transformation that will define the leaders from the laggards.

  • ReversingLabs Applies AI to Better Secure Application Binaries

    ReversingLabs Applies AI to Better Secure Application Binaries

    ReversingLabs this week launched a binary analysis tool that uses machine learning algorithms to identify risks before and after applications are deployed.

    Erik Thoen, vice president of product management for ReversingLabs, said Spectra Assure makes use of the same core artificial intelligence (AI) technologies the company uses to discover malware within binaries to analyze the entire software package, including first-, second- and third-party components, to identify potential threats.

    Thanks to the use of AI, Spectra Assure is able to scan applications that are gigabytes in size in minutes or hours, depending on their complexity, noted Thoen.

    That approach is critical because attacks launched against applications are more often aimed at binaries that are deployed in production applications rather than the source code used to create them, he added.

    In addition, Spectra Assure can be applied to both open source and proprietary software, said Thoen. In contrast, many legacy scanning tools are designed to only surface potential issues in open source software, he noted.

    Spectra Assure is designed to be used by both producers of software and the organizations that deploy software, said Thoen. Organizations that develop software need to be able to identify any security potential issues that may have arisen during the build process, while organizations that consume software need to be assured that there are no issues that will later compromise their IT environments, said Thoen.

    ReversingLabs is attempting to fill a gap that exists in many software supply chains between when a software build is completed and deployed, said Thoen. While developers may scan source code for vulnerabilities, it’s not uncommon for changes to be made during the build process that inadvertently create vulnerabilities, said Thoen.

    It’s still not clear which teams within organizations are assuming responsibility for the software supply chain. In many cases, DevSecOps teams are securing the software supply chain, but in other instances, cybersecurity teams have been tasked to lock them down. Among organizations that consume software, responsibility for application security might be assumed by either the cybersecurity teams or a procurement office responsible for purchasing software.

    Regardless of how software supply chain security is managed, the number of attacks against them has risen sharply. A recent ReversingLabs report found there has been a 1,300% Increase in malicious packages found in open source software platforms since 2020.

    Ultimately, it’s only a matter of time before more stringent regulations force the software supply chain security issue. A recent executive order issued by the Biden administration requiring federal agencies to secure software supply chains is a harbinger of stricter controls that enterprise IT organizations must implement. DevSecOps teams, in the meantime, have an opportunity to revisit the workflows that were created in an era where developers tended to overly trust the integrity of software components downloaded from open repositories.

    Of course, it may require a catastrophic event to truly galvanize organizations into action, but one way or another, application security is going to improve. The only issue remaining to determine is how expensive a proposition that will be given the potential fines that are likely to be one day levied.

  • Software Development in the Age of AI: How to Balance Quality and Speed

    Software Development in the Age of AI: How to Balance Quality and Speed

    There’s no mistaking the reality of AI’s influence. Across industries, businesses are calling on tech workers to make the most of this transformative technology as demand skyrockets and companies race to overcome the competition. A flagging economy only pushes expectations for AI use—in software especially. To contend with arduous workloads, 92% of developers are already using AI code generators, according to GitHub. That’s an overwhelming majority, not to mention that this usage is poised to continue generating steam.

    In light of this, we must ask ourselves a critical question: How can businesses ensure developers retain the quality of their AI-generated code? AI is a useful complement to human developers to maximize their time and allow them to focus on projects best suited to their skill sets. That said, companies have to put safeguards in place to harness the technology, not just for boosting developer productivity but also for quality outputs.

    AI Can’t Replace Human Developers

    AI code generators are a useful tool for a developer’s toolbox. Efficient, easy to use, and helpful (in a fairly rudimentary way in these early AI days), they give developers the ability to outsource some code-writing, which helps alleviate mounting demands as companies expect them to produce massive amounts of code — and fast. As most developers continue to buy into AI for productivity benefits, though, comes the responsibility to gut-check what these AI tools produce.

    There are multiple limitations and risk considerations when it comes to AI-generating code. Lack of source knowledge and context is one of them. AI pulls from what it’s taught (i.e., past data, natural language processing, machine learning), meaning it combines too many sources to trace specific pieces of coding back to one place; it’s impossible to trace the code back to a single developer or project. Security, too, becomes an issue; you can’t guarantee that what is produced is safe or clean as is. And, finally, AI doesn’t check for quality, which increases the likelihood that the code will be difficult and costly to maintain. All this ladders up to the mounting technical debt companies are already contending with.

    Developers can’t assume that any AI-generated code is of quality to be put directly into production. Human workers must review code thoroughly to ensure it meets clean code attributes–secure, reliable and maintainable. Ignoring these tenets puts businesses at risk of falling victim to the trillion-dollar cost of bad software. That said, there are still undeniable benefits to using AI in a coding environment that make it an invaluable tool for any company that wants to stay at the top of their software game.

    Working Responsibly With AI in Software Development

    Despite its potential shortcomings, when used safely and effectively alongside a clean code solution, AI brings a tremendous opportunity to the software space that has already made it a key tool for developers as their workloads and demands mount. For one, AI has the incredible ability to churn out code at a speed that far outpaces its human counterparts. This tech can complete tedious work that may take valuable time out of a developer’s day in a matter of seconds. AI can not only move a project forward at a faster rate, but its quick coding capabilities can also give developers a jump start on their projects and help them figure out where to begin.

    While the source of AI’s knowledge base can present a challenge, the vastness of that knowledge is a benefit. As long as a developer can input their preferred language and their specific needs, AI can share the best approach to solving a problem. Its algorithmic assistance can help lead a developer through parts of their projects step by step. This also alleviates the burden of research from developers, who can spend less time checking for accuracy than they would trying to understand different programming concepts or code snippets outside their area of expertise. AI can also handle the slog of documentation, which can not only bring clarity to what didn’t work during the development process but reduce time to delivery overall.

    Developers who don’t harness AI do so at the risk of falling behind the competition as this tech becomes such a vital piece of everyday work in software. But there’s no mistaking the fact that to best incorporate AI, developers still need to ensure its accuracy before any code generated by AI makes it to production.

    GenAI Requires Automated Tools as Safeguards

    As I previously said, developers can’t assume that any AI-generated code is clean and fit for production. That means human skills are still required to check the output. However, this kind of work can often fall to developers or DevOps, and having human workers check code on their own creates an incredible workload that can even negate the use of AI in the first place.

    The best way for companies to mitigate this and mitigate technical debt is to implement automation in the part of the process that scans and monitors AI-generated code. But that can’t be done without developer buy-in. Those creating the software are the people who will best understand the development process as they work through it day in and day out, so their input is required to make the most out of automation.

    There are developer tools that can handle this kind of work while still including developers in the process. For example, introducing code-scanning into the CI/CD process can not only help continuously monitor AI-generated code for bugs and issues, allowing developers to focus on other more challenging aspects of their projects, but it also gives them insight into errors and allows for speedier resolutions. These tools aren’t necessarily new to development, but their ability to identify bugs can help developers prioritize other aspects of the process, particularly as the amount of code reaches exponential levels compared to decades ago with the introduction of GenAI.

    Static application security testing (SAST) also allows for the identification of security vulnerabilities that can potentially arise from AI-generated code. This is critical in the software development lifecycle (SDLC) so developers can spot vulnerabilities before the code goes into production. AI-generated code is only as strong or secure as that source, and since it’s not entirely possible to verify, developers need to take every precaution available to avoid security issues that rack up costs in the long term.

    The vital part of using these tools is ensuring they’re integrated into the process in a way that makes sense for developers rather than creating additional work and headaches with a cumbersome process that doesn’t fit the way teams think and work. But, the need to check AI-generated code shouldn’t be underestimated. Developers can’t simply trust what they’re given. By including a level of automation within the development process—and doing so early and often—developers are in a position to produce better-quality, more secure software to push a business forward.

    Effective Adoption of AI Will Encourage Business Growth

    While it’s difficult to predict exactly where AI will take us in the years to come, it’s equally undeniable that it will play a critical part in the future of software development. Yes, AI is already helping to streamline processes and letting developers prioritize the tasks that best take advantage of their skills. But, as noted above, that doesn’t mean the code output is entirely safe, secure or accurate. Developers still need a level of automation to deliver code that best takes advantage of AI while keeping their workload manageable and their focus on other parts of the development process.

    With the right guardrails in place, developers can guide AI to create the kind of clean, secure, and reliable code that aids businesses rather than create costly problems for them, both now and down the line. Human skills, with a boost from AI, plus code scanning safeguards, give organizations the winning combination to put themselves in the best possible place to succeed in a rapid, fierce competitive landscape of digital business.

  • Legit Security Adds Sensors to Detect Usage of Gen AI Tools to Write Code

    Legit Security Adds Sensors to Detect Usage of Gen AI Tools to Write Code

    Legit Security today announced it has added the ability to detect when developers use generative artificial intelligence (AI) tools to write code to its application security posture management (ASPM) platform.

    The ASPM platform developed by Legit Security already has discovery capabilities for application secrets, for example, enabled by sensors that are now being extended to include generative AI tools.

    Legit Security CTO Liav Caspi said the overall goal is to allow DevSecOps teams to identify who uses these tools to write which types of code. Each DevSecOps team can then decide how stringently to enforce policies to either ban or limit usage of those tools, he added.

    There is no doubt that generative AI tools are increasing developer productivity, but the large language models (LLMs) that provide the ability to generate code were trained using examples of code of varying quality collected from across the web. Much of that code either contains known vulnerabilities or is simply inefficient, which can manifest itself in the output surfaced by a generative AI platform.

    The Legit Security ASPM platform, in addition to identifying generative AI tools being used in real-time, can also be employed to scan the LLM itself to identify security risks such as prompt injection and insecure output handling. Unfortunately, most cybersecurity teams are unaware of the extent to which application code generated by a machine might be insecure, so it currently falls to leaders of DevSecOps teams to determine what is an acceptable use of these tools. It may be perfectly fine to rely on a generative AI tool to create a script that will be used for an internal-facing application. However, using the same tool to write business logic that will be exposed to the internet may not be especially prudent.

    It’s not clear how much of the code finding its way into software builds is being generated by a machine versus a human. In the longer term, the goal is to be able to identify that code by, for example, detecting substantial changes in the way code is normally written by a human developer, said Caspi.

    Of course, while an ASPM may detect usage of generative AI tools that are connected to a corporate environment, there are still plenty of opportunities for developers to use these tools offline to create code that can later be added to a build using a code check-in tool. After all, many developers have used various shadow IT tools to write code for decades. Regardless of who or what wrote that code, however, it should still be reviewed.

    In the meantime, the overall size of the codebase that DevSecOps teams will need to manage and secure will only exponentially increase as developers become more productive. Hopefully, more AI capabilities will soon be embedded into DevOps platforms to further automate the management of software builds at a time when many DevOps teams were already finding themselves overwhelmed even prior to the rise of generative AI.

  • Cisco Extends Scope and Reach of Observability Platform

    Cisco Extends Scope and Reach of Observability Platform

    Cisco this week expanded the scope of its observability platform by adding predictive and generative artificial intelligence (AI) capabilities along with log analytics and a Core Web Vitals tool to make it simple to ensure web pages don’t lose their search engine ranking because of poor user experience.

    In addition, Cisco is now leveraging extended Berkeley Packet Filters (eBPF) in the kernel of Linux operating systems to provide visibility into Kubernetes workloads and a session replay capability for cloud-native applications. In addition, Cisco has allied with Komodor, a provider of a Kubernetes management platform, to gain additional visibility into these environments.

    Cisco also added digital experience monitoring (DEM) capabilities based on real user monitoring (RUM) and the full-stack observability it provides following its recent acquisition of Accedian, a provider of an analytics platform for monitoring end-user experiences.

    Finally, Cisco added a Data Security Posture Management (DSPM) Observability module to the company’s Business Risk Observability platform based on the same core platform.

    Announced at a Cisco Live EMEA event, these latest updates are part of an ongoing Cisco effort to extend the reach of its observability platform well beyond DevOps workflows.

    Gregg Ostrowski, executive CTO for Cisco AppDynamics, said as part of that effort, Cisco is making it simpler to aggregate telemetry data for the AI for IT operations (AIOps) capabilities it provides by pulling directly from platforms such as Cisco ThousandEyes and Cisco DNA Center as well as from third-party partners such as VMware, Zabbix and ServiceNow. Similarly, Cisco is now leveraging eBPF to collect data from Kubernetes environments.

    Ostrowski added that the overall goal is to reduce the amount of instrumentation effort that IT teams would have to undertake to instrument highly distributed IT environments.

    There will still be a need to instrument applications, but the collection of telemetry data from the underlying IT infrastructure environment is going to be increasingly automated, he noted. In the meantime, it’s become less expensive to instrument applications using open source OpenTelemetry agent software, added Ostrowski.

    In general, Cisco, in addition to enabling organizations to rationalize existing tools, is also making a case for improving collaboration by, for example, making use of natural language capabilities enabled by generative AI to launch queries and provide summarizations.

    At the same time, Cisco is partnering to add additional capabilities, such as the ability to monitor machine learning algorithms via an alliance with Aporia and AS/400 systems via an alliance with Perform IT.

    Ultimately, Cisco is betting that observability across the entire enterprise IT environment is going to become more unified as organizations increasingly look to correlate business processes with specific IT events, including indications of cybersecurity breaches. It’s not clear how far down that path enterprise IT organizations are just yet, but given the total cost of observing and monitoring IT environments today, there is more pressure than ever to find more cost-effective approaches to holistically managing IT. The challenge, as always, is unifying all the existing fiefdoms in IT organizations that have already embraced a wide variety of tools and platforms they are hesitant to give up.

  • 10 Key Features of AI Code Assistants

    10 Key Features of AI Code Assistants

    One of the most potent tools that have emerged to streamline the coding process is code assistants. Code assistants are software tools designed to assist programmers by providing suggestions and automated features that help in writing code more efficiently and accurately. They act as virtual aides, making the coding process faster, more intuitive and less prone to errors.

    Code assistants can be categorized into two types: Rule-based and AI-based. Rule-based assistants follow predefined rules and perform according to set guidelines. They are mainly used for syntax highlighting, code formatting and basic error detection. AI-based code assistants can learn from past coding patterns, predict what a programmer is trying to achieve and suggest appropriate code snippets. They are capable of understanding the context of the code and providing accurate suggestions, as well as generating entire blocks of code based on natural language instructions. Read this blog post for examples of popular AI coding assistants.

    Whether you are an experienced software developer handling complex projects or a beginner, coding assistants can automate the tedious aspects of coding, allowing you to focus more on problem-solving and innovation.

    The Growing Importance of AI in Software Development

    As software development becomes more complex and time-sensitive, the role of AI in this field has grown significantly. AI-driven code assistants represent a major shift in how software is developed. They bridge the gap between human expertise and the efficiency of automated processes. 

    AI in software development not only speeds up the coding process but also enhances the quality of the code. These tools can analyze vast amounts of code, learn from it and apply that learning to assist developers in real-time. They can adapt to different coding styles and requirements, making them versatile tools for a wide range of programming tasks. Furthermore, as software projects increase in size and complexity, AI code assistants become crucial in managing and navigating these complexities. They offer intelligent insights, detect potential issues before they become problematic, and assist in keeping large codebases clean and manageable.

    In addition to increasing efficiency, AI code assistants are instrumental in reducing the barrier to entry in software development. They make coding more accessible to people with varying levels of expertise, including those who are new to programming. This accessibility is essential in an era where the demand for software development skills is continuously rising. By providing real-time assistance and learning resources, AI code assistants are not just tools for current developers but also learning aids for future generations of programmers.

    10 Key Features to Expect from Code Assistants in 2024

    With the rapid advances in large language models (LLMs), the following features are becoming standard in coding assistants as we enter 2024.

    Advanced Contextual Understanding

    Modern code assistants are evolving to understand the context of code more intricately. Unlike simple autocomplete tools, these AI systems can analyze the broader scope of a project, recognizing patterns and offering suggestions that align with the overall architecture and design patterns. This advanced understanding not only streamlines coding but also aids in maintaining consistency throughout the codebase.

    Real-Time Code Optimization Suggestions

    AI tools can proactively recommend changes to improve performance and efficiency. For instance, a code assistant might suggest a more efficient algorithm or highlight redundant code blocks that can be simplified. This feature not only enhances code quality but also serves as a learning tool for developers, exposing them to best practices and alternative approaches.

    Enhanced Debugging Assistance

    Debugging is often a time-consuming aspect of development. In 2024, expect code assistants to revolutionize this process by offering enhanced debugging assistance. Through deep analysis, these tools automatically detect errors and anomalies, suggesting potential fixes. They can pinpoint the exact location of a bug and provide context, significantly reducing the time developers spend in the debugging phase.

    Automated Refactoring

    Refactoring is crucial for maintaining clean code, and AI-driven assistants now offer sophisticated automated refactoring capabilities. They intelligently suggest changes that improve code readability and maintainability, such as restructuring code or renaming variables for clarity. These suggestions will be particularly beneficial for managing and updating legacy code, ensuring it adheres to modern coding standards.

    Code Documentation Generation and Update

    Keeping documentation updated is a challenge. Code assistants can now automate this task by generating and updating documentation in real-time. As code changes, the documentation will reflect these updates, ensuring that it always stays in sync with the codebase. This feature will be invaluable in large teams where keeping track of changes can be daunting.

    Predictive Coding Assistance

    Moving beyond reactive suggestions, code assistants will offer predictive assistance. By analyzing coding patterns and project structures, these tools will predict a developer’s next steps and offer relevant suggestions even before they are explicitly requested. This proactive approach will speed up the development process, reducing the cognitive load on developers.

    Understanding and Responding to Developer Queries in Natural Language

    Developers can pose queries or express coding requirements in plain English, and the assistant will interpret and execute these requests. Chat-based interfaces offered by leading AI coding assistants take this one step further. This advancement will make coding more intuitive and accessible, especially for beginners or non-expert users.

    Real-Time Suggestions and Conflict Resolution in Team Projects

    Collaboration in coding projects can lead to conflicts and inconsistencies. Code assistants now offer real-time suggestions and automated conflict resolution for team projects. These tools will identify potential conflicts in merge requests and suggest optimal resolutions, enhancing team collaboration and maintaining code integrity.

    Personalized Coding Assistance Based on Past Interactions

    Personalization is becoming a significant feature of code assistants. By learning from past interactions with a developer, these tools will tailor their suggestions and support to align with individual coding styles and preferences. This adaptive learning approach ensures that the assistance provided is most relevant and efficient for each developer.

    Enhancing Accessibility of Coding for Non-Expert Users

    One of the most impactful features of code assistants is their role in democratizing coding. By simplifying complex coding concepts and automating mundane tasks, these tools will make software development more accessible to non-experts. Beginners and those learning new programming languages will find these assistants particularly useful, as they provide guidance and learning aids.

    Conclusion

    Code assistants powered by AI are becoming an integral part of the software development process. They not only make coding faster and more efficient but also reduce the chances of errors, making the resultant software more reliable. The role of code assistants is only expected to grow, bringing in more advanced and intuitive features. Whether you are a seasoned programmer or a newbie, embracing code assistants can be a game-changer for your coding journey.