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Author: Deborah Schalm

  • ShiftLeft Announces New Developer-Focused Next Generation Static Analysis Solution that Increases Application Security Productivity by 5X

    ShiftLeft’s customer data demonstrates the new release of its SAST solution allows organizations to fix 70% of new vulnerabilities in less than three weeks, proving the power of inserting security into the modern SDLC

    Santa Clara, Calif. – June 30, 2020 – ShiftLeft, Inc., an innovator in automated application security, released a new version of NextGen Static Analysis (NG SAST), including new workflows, purpose-built for developers that significantly improve security, while enhancing productivity. ShiftLeft’s customer data confirms that developer productivity suffers when security isn’t automated and seamlessly integrated into the software development lifecycle (SDLC).

    Security productivity challenges are rooted in the disconnect between the modern SDLC and the incremental improvements from legacy application security tools, which were designed for ad hoc scanning in the legacy waterfall mode of software development. With staffing ratios often in excess of 200 developers for every AppSec professional, scaling security to meet the requirements of the agile SDLCs requires increasing both developer engagement and efficiency.

    Developers Overwhelmingly Believe Disconnect with Security Inhibits Productivity

    In a new survey of over 165 developers, AppSec and DevOps professionals, ShiftLeft found that 96% of developers believe the disconnect between developer and security workflows inhibits developer productivity. Furthermore, when asked to prioritize, AppSec professionals ranked creating developer-friendly security workflows as their top priority, which was even higher than protecting applications in production environments.

    “Deprioritization of security has been the most common approach to balancing AppSec with developer productivity because automating security in developer workflows has historically been prohibitively expensive for all but the most elite security organizations,” said Izak Mutlu, former VP of Information Security at Salesforce.com. “ShiftLeft’s NG SAST combines industry-leading scan speed, accuracy and a seamless workflow for rapid collaboration between development and AppSec teams so organizations of all sizes can run their AppSec initiatives at the pace of software development.”

    The rise of long-term and permanent remote work has increased the amount of business being done online, therefore increasing the number of web properties and applications that need to be developed and supported. As organizations demand software to be built and delivered at an ever-increasing velocity, enhancing developer productivity while enhancing security is critical. The survey revealed that performing security scans too late in the SDLC (89.7%) and lack of remediation guidance (87.7%) are also significant inhibitors to developer productivity.

    ShiftLeft’s New Developer-Driven Workflows Significantly Increase Productivity and Quality of Application Security

    To scale security and address developer productivity challenges, ShiftLeft’s new version of NG SAST delivers holistic workflows with developer engagement and productivity as a first principle. The new developer-driven security workflow relies on the git-based process that developers already use to write and update code. This allows organizations to:

    • Automate code analysis with every pull/merge request
    • Deliver immediate and accurate security feedback directly to each developer making the change
    • Enable developers to fix vulnerabilities, in the same way they address bugs, without leaving their development environment
    • Enable AppSec teams to write security-focused build rules that accept or deny merges, thereby allowing AppSec to scale
    • Help developers adopt secure coding best practices through Security Insights
    • Eliminate scanning bottlenecks with unlimited concurrent scans
    • Protect intellectual property by scanning without taking source code outside of their organization
    • Rapidly deploy with self-service on-boarding that doesn’t require network architecture updates, new firewall configurations or expensive professional services
    • Further customize workflows through comprehensive APIs

    This developer-centric approach to code analysis greatly increases security and productivity by delivering the right vulnerability to the right developer at the right time. Mean time to remediation (MTTR) is reduced because vulnerabilities get fixed while the code is still fresh in the developers’ minds, and vulnerable code doesn’t become deeply interconnected because security build rules prevent it from entering the master branch.

    “ShiftLeft’s NextGen Static Analysis gave us the speed and accuracy that we needed to create security feedback loops for our development team without altering their workflows. By scanning every pull request our software engineers are able to fix vulnerabilities far more efficiently,” said Thomas Heuckeroth, VP CyberSecurity at The Emirates Group. “Not only are we seeing month-over-month decline in MTTR, but it’s now common for vulnerabilities to get fixed in the same sprint they are found and, most importantly, our engineers really like the process.”

    ShiftLeft customers who automate NG SAST at the pull request increase scanning frequency by 110X over the industry average. Furthermore, by providing security feedback in the developer’s workflow, customers experience a 4.9X reduction in MTTR, within 90 days of going live. The result is 70% of new vulnerabilities get fixed in a typical three week sprint before making it into production. By spending less time on fixing vulnerabilities and more time writing new code, developers can increase productivity while enhancing security.

    “The only way to deliver security at the pace of modern SDLCs is to create a culture of individual developer accountability for the security of the code they write. However, this demands new AppSec solutions purpose-built for today’s requirements,” said Manish Gupta, CEO of ShiftLeft. “Based on our new survey, it’s clear developers feel ad hoc security processes and the tools they have available to them today aren’t helping. We’ve always put productivity and security at the foundation of our platform, and our customers’ results demonstrate that the new workflow is significantly improving their security postures while increasing developer productivity.”

    To learn more about ShiftLeft NG SAST, visit the ShiftLeft website and sign up for free here: www.shiftleft.io/register.

    About ShiftLeft

    ShiftLeft’s NextGen Static Analysis (NG SAST), purpose-built to insert security into developer workflows. NG SAST’s speed and accuracy enables security automation with every pull request, which provides the right developer with the right vulnerability information at the right time. Hence, vulnerabilities get fixed faster and earlier, which drives down mean-time-to-remediation (MTTR), reduces attack surfaces and minimizes technical debt accrual. Furthermore, NG SAST goes beyond technical vulnerabilities (e.g., The OWASP Top Ten) to identify cloud-centric vulnerabilities that traditional static analysis tools can’t find, such as business logic flaws, data leakage, hard-coded literals and insider threats.

    To learn how ShiftLeft keeps application security in sync with the rapid pace of DevOps, see https://www.shiftleft.io/.

     

     

     

     

     

     

     

  • PionerasDev wins IBM Open Source Community Grant to increase women’s participation in programming

    By Todd Moore, Guillermo Miranda

    Last fall, IBM’s open source community announced a new quarterly grant to award nonprofit organizations that are dedicated to education, inclusiveness, and skill-building for women, underrepresented minorities, and underserved communities in the open source world. The Open Source Community Grant aims to help create new tech opportunities for underrepresented communities and foster the adoption and use of open source.

    Today, we’re proud to share that this quarter’s grant is awarded to Colombia-based PionerasDev, a nonprofit whose mission is to create and cultivate self-managed communities for women to learn and work on computer and web programming. The organization helps women and girls in Colombia learn how to code, provides them with a supportive community to foster their self-education through events, meetups and mentorships and even helps them to find jobs in tech.

    IBM’s open source community nominated many incredible nonprofits doing important work, but the community was especially inspired by the PionerasDev’s commitment to increasing the participation of women in the programming and developing space, and we are proud to be able to do our part to help it further its goals.

    Free events and mentorship advance tech in Latin America
    PionerasDev is a women-led and women-focused organization, with physical locations across Colombia and members across the globe. The organization, which has grown from five members at launch to over 1,200 in just three years, is rapidly growing across Colombia and creating a much-needed community of women to teach and support each other in the open source and development world. Pioneras focuses primarily on recruiting women from lower income areas, with over 80% of its current membership falling into that category.
    Over the past three years, PionerasDev has held hundreds of free events for women across Colombia, including meetups and bootcamps as well as activations at the country’s largest tech and open source conferences.

    “We believe that creating a supportive and empowering community for women and girls to learn how to code and program is a crucial first step to increasing the number of women we see in tech jobs across the country. Too often, the barrier for women to enter these web and computer programming jobs is the lack of access to resources and training, so at Pioneras we’re working to alleviate that and provide women from lower income areas with the tools and support they need to succeed,” said Marian Villa Roldàn, CEO of PionerasDev.
    “We began as a small group of women focused on Node.js technology, and when we launched, the women who were joining our meetups thought they were there for digital marketing training. Coding and development weren’t even on the radar. But now, through countless events and a growing community, we’re showing women that they too can be active in the tech and open source worlds.”

    PionerasDev plans to put the IBM Open Source Community Grant to use to reach and impact more women through online events and boot camps. With the state of the pandemic, a large number of the 40 scheduled events the organization had in 2020 have had to be rescheduled or changed to virtual events, so they plan to apply these resources to ensure their mission isn’t affected or slowed down due to the pandemic. Additionally, the nonprofit has plans for a special project in the rural area of Colombia and the launch of the organization’s first ever tech conference, Pionera Conference.
    What is the Open Source Community Grant?
    IBM has a longstanding commitment to investing in diversity and inclusion. The IBM Open Source Community Grant recognizes a nonprofit organization dedicated to education and skill building for women, under-represented minorities, and underserved communities, while also promoting open source. The grant includes a cash award ($25K) and a technology award (valued at $25K) to directly support education and career development activities. Our internal open source community votes on the winners, with Girls Who Code winning the inaugural award in October 2019 and Outreachy being awarded the second in April of 2020.
    Todd Moore is the Vice President of Open Technology and Developer Advocacy at IBM and Guillermo Miranda is the Vice President and Global Head of Corporate Social Responsibility at IBM.

  • IBM and LFAI move forward on trustworthy and responsible AI

    By Todd Moore, Sriram Raghavan, Aleksandra Mojsilovic

    For over a century, IBM has created technologies that profoundly changed how humans work and live: the personal computer, ATM, magnetic tape, Fortran Programming Language, floppy disk, scanning tunneling microscope, relational database, and most recently, quantum computing, to name a few. With trust as one of our core principles, we’ve spent the past century creating products our clients can trust and depend on, guiding their responsible adoption and use, and respecting the needs and values of all users and communities we serve.
    Our current work in artificial intelligence (AI) is bringing a transformation of similar scale to the world today. We infuse these guiding principles of trust and transparency into all of our work in AI. Our responsibility is to not only make the technical breakthroughs required to make AI trustworthy and ethical, but to ensure these trusted algorithms work as intended in real-world AI deployments.
    Our theoretical work in fair machine learning led to one of the earliest and most cited bias mitigation algorithms. We applied advanced machine learning methods for mitigating unwanted discrimination against protected groups and individuals. The integration of our bias detection and mitigation algorithms into Watson OpenScale enabled IBM to be the first vendor to address AI bias in a real product. As a culmination of this work, we created AI Fairness 360, a comprehensive open source toolkit for handling bias in machine learning algorithms. Additionally, we have two more open source Trusted AI toolkits: Adversarial Robustness 360, a toolbox and library for defending AI from adversarial attacks, and AI Explainability 360, a toolbox that explains decisions of AI systems.
    On June 18, 2020, the Technical Advisory Committee of Linux Foundation AI Foundation (LFAI) has voted positively to host and incubate these Trusted AI projects in LFAI. We are going through the onboarding process to formalize the charter and governance and IT logistics of these projects and move them under the foundation. Stay tuned for an LFAI blog to announce the details.
    Donation of these projects to LFAI will further the mission of creating responsible AI-powered technologies and enable the larger community to come forward and co-create these tools under the governance of Linux Foundation.
    Trusted AI 360 Toolkits

    • The AI Fairness 360 (AIF360) Toolkit is an open source toolkit that can help detect and mitigate unwanted bias in machine learning models and datasets. It provides approximately 70 metrics to test for biases, and 11 algorithms to mitigate bias in datasets and models. The AI Fairness 360 interactive experience provides a gentle introduction to the concepts and capabilities. Recently, AIF360 also announced compatibility with Scikit Learn, and an interface for R users.
    • The Adversarial Robustness 360 (ART) Toolbox is a Python library for machine learning security. ART provides tools that enable developers and researchers to evaluate, defend, certify and verify machine learning models and applications against the adversarial threats of evasion, poisoning, extraction, and inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, generation, certification, etc.).

    Last year, DARPA awarded IBM Research scientists with a grant to advance research in adversarial AI

    • The AI Explainability 360 (AIX360) Toolkit is a comprehensive open source toolkit of diverse algorithms, code, guides, tutorials, and demos that support the interpretability and explainability of machine learning models. The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas.

    Continuing the mission with LFAI Trusted AI Committee
    Last year, IBM worked with LFAI to establish the Trusted AI Committee with a mission of advancing the practice of trustworthy AI. The committee has since grown to include over 10 organizations, working towards defining and implementing principles of trust in AI deployments. One of the activities this committee has been driving is the integration of Trust 360 toolkits in Apache Nifi or Kubeflow Pipelines, as a way of powering-up trustworthy machine learning workflows.
    We also recognize that when it gets to promoting trustworthy, beneficial, and equitable AI, technology is only one part of the equation. We have the responsibility to look at the broader context of how the AI systems are being designed and deployed, how they are being used and by whom, and evaluate their impact on users and communities.
    On this mission, contributions from social science, policy and legislation, and diverse perspectives play equally important role as the technology itself. We look forward to contribution from different areas and stakeholders and invite them to join the community and contribute their work towards advancing the mission of the foundation and its Trustworthy AI open source projects.
    More information on the activities of the LFAI Trusted AI Committee can be found in the presentation to LFAI Governing board. Those interested in joining can do so via Trusted AI Committee page.

  • IBM Differential Privacy Library: The single line of code that can protect your data

    By Naoise Holohan, Research Staff Member, Privacy and Security, IBM Research Europe

    This year for the first time in its 230-year history the US Census will use differential privacy to keep the responses of it’s citizens confidential when the data is made available. But how does it work?

    Differential privacy uses mathematical noise to preserve individuals’ privacy and confidentiality while allowing population statistics to be observed. This concept has a natural extension to machine learning, where we can protect models against privacy attacks, while maintaining overall accuracy.While the US government built its own differential privacy tool, IBM has been working on its own open source version and today we are publishing our latest release v0.3. The IBM Differential Privacy Library boasts a suite of tools for machine learning and data analytics tasks, all with built-in privacy guarantees.For example, if you want to know my age (32) I can pick a random number out of a hat, say ±7 – you will only learn that I could be between 25 and 39. I’ve added a little bit of noise to the data to protect my age and the US Census will do something similar.

    Our library is unique to others in giving scientists and developers access to lightweight, user-friendly tools for data analytics and machine learning in a familiar environment – in fact, most tasks can be run with only a single line of code.

    What also sets our library apart is our machine learning functionality enables organisations to publish and share their data with rigorous guarantees on user privacy like never before.

    Technical detailsWith v0.3, the library now comes with a budget accountant to track privacy budget spend across different operations. Using advanced composition techniques, the budget accountant allows users to extract even more insight than simpler accounting methods and while it’s hard to quantify, under typical workloads, privacy budget savings in excess of 50 percent are not uncommon.

    Our library includes an array of functionality to extract insight and knowledge from data with robust privacy guarantees. We have focused on developing solutions for the most popular algorithms, including histograms, logistic regression, k-means clustering and principal component analysis (PCA), as well as giving developers the basic building blocks of differential privacy to allow them to develop their own custom solutions.

    The library includes the following key components which don’t exist in similar libraries currently available:

    • Accountant: Track and limit privacy spend across multiple operations;
    • Mechanisms: A comprehensive collection of the basic building blocks of differential privacy, used to build new tools and applications;
    • Machine learning: Machine learning algorithms for pre-processing, classification, regression and clustering.

    Also included is a collection of fundamental tools for data exploration and analytics. All the details for getting started with the library can be found at IBM’s Github repository.

  • Take advantage of open source trusted AI packages in IBM Cloud Pak for Data

    By Todd Moore, Vice President, Open Technology, IBM

    As open source artificial intelligence technologies grow, the need for AI systems to make decisions fairly, to be invulnerable to tampering, and to be explainable is more important than ever. At IBM, we believe that building trust in AI starts in the open, with code that is transparent and accessible to anyone. In light of our commitment to trusted AI, IBM has made it easy to securely access three of our trusted AI packages in IBM Cloud Pak for Data.

    Specifically, we’re adding AI Fairness 360 Toolkit, the AI Explainability 360 Toolkit, and the Adversarial Robustness Toolbox to IBM Cloud Pak for Data so that our users can take advantage of key packages that help them make decisions about their data in a more informed way.

    • AI Fairness 360 Toolkit (AIF360): Available in both Python and R, the AI Fairness 360 package includes a comprehensive set of metrics (over 70) for datasets and models to test for biases, explanations for these metrics, and algorithms (about 11) to mitigate bias in datasets and models. AIF360 translates algorithmic research from the lab into the actual practice of domains as wide-ranging as finance, human capital management, healthcare, and education. Find guidancefor using the tool.
    • AI Explainability 360 Toolkit (AIX360): An open-source library that supports interpretability and explainability of datasets and machine learning models throughout the AI application lifecycle. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. Find guidancefor using the tool.
    • Adversarial Robustness Toolbox (ART): A Python library supporting developers and researchers in defending machine learning models against adversarial threats (including evasion, extraction and poisoning) and helps making AI systems more secure and trustworthy. ART includes attacks for testing defenses with state-of-the-art threat models. Find tutorials about notebooksand examplesfor using the tool.

    Users of IBM Cloud Pak for Data can take advantage of these packages via the Open Source Management service within the product.

    Using trusted AI packages in the Open Source Management service

    In May, IBM announced the availability of the Open Source Management (OSM) service in Cloud Pak for Data 3.0, our fully integrated data and AI platform that modernizes how your business collects, organizes, and analyzes data to infuse AI throughout the organization.

    The service provides access to a curated list of 800 open source packages and walks you through a governed workflow to request package offering insights to security and vulnerability risks. The result is that you’re better able to understand what open source packages are used the most, who in your organization is using the packages, and vulnerabilities related to each project.

    You can use the trusted AI packages via the Open Source Management service in IBM Cloud Pak for Data. After you install the OSM service, you can choose two different ways to interact with the packages:

    Option 1 – View insights

    This option shows a holistic view of the number of projects that use open source packages, the number of packages that use each type of license, the number of packages affected by vulnerabilities, number of times users interact with open source packages, and the most active users across all packages.

    Option 2 – View packages

    This option enables you to view all the available packages. Each package includes a summary, vulnerability check report, supporting documents related to the package, list of projects using the package and a provision to link to new project. Once requested, approval process starts. After approval, you can use it securely in the linked projects and manage it using “View Insights”.

    Check out our documentationto learn more about Open Source Management service. If you’re ready to try Cloud Pak for Data for yourself, consider a 7-day trial at no cost.

  • Red Hat Ansible Automation Platform Enhancements and New Certified Ansible Content Collections Refine the Automation Experience to Drive Business Imperatives

    Ready-to-use, curated automation for a wide range of platforms, public clouds, network and security technologies help organizations more easily get started with the latest trusted automation

    RALEIGH, N.C. —  —Red Hat, Inc., the world’s leading provider of open source solutions, today announced key enhancements to the Ansible Automation portfolio, including the latest version of Red Hat Ansible Automation Platform and new Red Hat Certified Ansible Content Collections available on Automation Hub. The latest release of Red Hat Ansible Automation Platform helps organizations expand automation to new domains while increasing productivity and cross-team collaboration. As a component of the latest platform release, new Ansible Content Collections developed, tested, and supported by Red Hat enable organizations to get the most up-to-date automation content.

    A new report from Forrester Research highlights that, “Your intelligent automation roadmap and maturity will make you a winner (or a loser).”1 In light of shifting global dynamics, automation has moved to the forefront of IT priorities in an effort to help organizations move faster and more efficiently. To get the most out of automation, however, IT teams need more than just an automation tool — they require smarter, more reliable automation technologies.

    Red Hat Ansible Automation Platform helps organizations accelerate their automation efforts with access to the latest Ansible innovations and technology as they need it, while still providing the security features required for production ready workloads. In addition to Ansible Content Collections, the latest version of the Red Hat Ansible Automation Platform includes the availability of Ansible Tower 3.7, the introduction of automation services catalog, and updates to Automation Analytics.

    A faster path to the latest tested and trusted Ansible automation innovations
    As automation requirements evolve, organizations need the ability to extend automation capabilities to help them expand into new domains more quickly and safely. First announced with Red Hat Ansible Automation Platform in Fall 2019, Ansible Content Collections provides a continuously updated set of curated automation content, helping to shorten the time to value and improve the efficiency of maintaining and consuming automation by enabling organizations to reuse Ansible content at scale.

    With the launch of Red Hat-maintained Ansible Content Collections, Red Hat is publishing 17 new Collections that are all written and supported by Red Hat. Collections are available for many popular network, security and cloud partners, including AWS, Arista, Cisco, IBM, Juniper, Splunk, VyOS platforms and more. This feature can help organizations deploy automation with greater confidence, knowing that it has been tested and is backed by Red Hat’s award-winning support. Collections are published asynchronously from Ansible product releases, making it easier for organizations to access the latest Collections as they become available. With the addition of these Red Hat-written Collections, there are now 49 certified Collections available from Red Hat and partners in Automation Hub.

    A full list of supported and certified Ansible Content Collections can be found at cloud.redhat.com.

    Deeper integration across Red Hat’s management portfolio
    In addition to extending automation for partner technologies, Ansible Content Collections help further integrate Red Hat’s management products. New Collections are available for:

    To meet evolving business demands in the face of uncertain global conditions, we see IT organizations accelerating their automation efforts to help drive faster innovation while managing demand surges due to remote workforces. The latest release of Red Hat Ansible Automation Platform helps to further meld automation innovation with production stability, offering greater confidence to organizations that they can adopt new automation technologies to expand IT footprints while still supporting mission-critical environments.

    THOMAS ANDERSONVICE PRESIDENT, ANSIBLE AUTOMATION, RED HAT
    • Ansible Tower Content Collection enables organizations to configure, manage, and operate their Ansible Tower environments at scale. With the Collection, users can create and modify inventories, credentials, and Ansible Tower job templates, as well as launch and monitor Ansible Tower jobs.
    • Red Hat Insights Content Collection enables organizations to automate manual tasks by using the Red Hat Insights API. These common tasks include installation and registration of Red Hat Insights clients, executing Ansible Playbooks for issue remediation and, through integration with Ansible Tower, automating compliance activities and serving as a dynamic inventory source.
    • Red Hat Satellite Content Collection enables organizations to automate manual tasks by using the Red Hat Satellite API. Users can build and configure a Satellite environment from start to finish, providing complete end-to-end management of Red Hat Satellite via Ansible.

    New levels of intelligence and increased ease of use
    In addition to Ansible Content Collections, the latest release of Red Hat Ansible Automation Platform includes new features and enhancements that help simplify automation for new as well as experienced users while increasing the speed and flexibility of Ansible. These updates include:

    • Red Hat Ansible Tower 3.7 adds new capabilities that help enable customers to engage automation faster and at a larger scale. New updates include tweaks to help eliminate bottlenecks in job processing, as well as optimizing the processing of common API queries and a rework of the user interface communication protocols to maintain performance.
    • Automation Analytics automation calculator, an addition to Automation Analytics, enables users to evaluate the value of automation at their organization and customize it to their individual company’s data. It can measure past, current and future return on investment, providing real-time metrics around effectiveness to encourage wider adoption of automation across more domains.
    • Automation services catalog provides lifecycle management for automation resources, spanning the provisioning, retirement and logging of Ansible platforms across physical, virtual, cloud or container-based environments. This delivers greater visibility over what is being automated and helps organizations to maintain the controls and compliance prescribed by their business.

    Availability
    The latest innovations available in Red Hat Ansible Automation Platform, including Ansible Content Collections, are available now on cloud.redhat.com. New Collections will be released on a rolling basis.

    1Your Automation Psychology And Roadmap Just Shifted Gears, Craig LeClair, Forrester Research, May 2020

    ABOUT RED HAT

    Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver reliable and high-performing Linux, hybrid cloud, container, and Kubernetes technologies. Red Hat helps customers integrate new and existing IT applications, develop cloud-native applications, standardize on our industry-leading operating system, and automate, secure, and manage complex environments. Award-winning support, training, and consulting services make Red Hat a trusted adviser to the Fortune 500. As a strategic partner to cloud providers, system integrators, application vendors, customers, and open source communities, Red Hat can help organizations prepare for the digital future.

    FORWARD-LOOKING STATEMENTS

    Certain statements contained in this press release may constitute “forward-looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements as a result of various important factors, including: risks related to our pending merger with International Business Machines Corporation, the ability of the Company to compete effectively; the ability to deliver and stimulate demand for new products and technological innovations on a timely basis; delays or reductions in information technology spending; the integration of acquisitions and the ability to market successfully acquired technologies and products; risks related to errors or defects in our offerings and third-party products upon which our offerings depend; risks related to the security of our offerings and other data security vulnerabilities; fluctuations in exchange rates; changes in and a dependence on key personnel; the effects of industry consolidation; uncertainty and adverse results in litigation and related settlements; the inability to adequately protect Company intellectual property and the potential for infringement or breach of license claims of or relating to third party intellectual property; the ability to meet financial and operational challenges encountered in our international operations; and ineffective management of, and control over, the Company’s growth and international operations, as well as other factors contained in our most recent Quarterly Report on Form 10-Q (copies of which may be accessed through the Securities and Exchange Commission’s website at www.sec.gov), including those found therein under the captions “Risk Factors” and “Management’s Discussion and Analysis of Financial Condition and Results of Operations”. In addition to these factors, actual future performance, outcomes, and results may differ materially because of more general factors including (without limitation) general industry and market conditions and growth rates, economic and political conditions, governmental and public policy changes and the impact of natural disasters such as earthquakes and floods. The forward-looking statements included in this press release represent the Company’s views as of the date of this press release and these views could change. However, while the Company may elect to update these forward-looking statements at some point in the future, the Company specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing the Company’s views as of any date subsequent to the date of this press release.

    ###

    Red Hat, Red Hat Enterprise Linux, the Red Hat logo, JBoss, Ansible, Ceph, CloudForms, Gluster and OpenShift are trademarks or registered trademarks of Red Hat, Inc. or its subsidiaries in the U.S. and other countries. Linux® is the registered trademark of Linus Torvalds in the U.S. and other countries. The OpenStack Word Mark is either a registered trademark/service mark or trademark/service mark of the OpenStack Foundation, in the United States and other countries, and is used with the OpenStack Foundation’s permission. Red Hat is not affiliated with, endorsed or sponsored by the OpenStack Foundation, or the OpenStack community.

  • Keysight Technologies Acquires Eggplant

    Brings together two complementary companies to create an innovative force in the intelligent software test automation market

    Keysight Technologies, Inc. (NYSE: KEYS), a leading technology company that helps enterprises, service providers and governments accelerate innovation to connect and secure the world, and Eggplant, the digital automation intelligence specialist, today announced that Keysight has completed the acquisition of Eggplant from The Carlyle Group.

    Eggplant is an industry leading software test automation platform provider that uses artificial intelligence (AI) and analytics to automate test creation and test execution. Eggplant’s Digital Automation Intelligence platform can test any technology on any device, operating system or browser at any layer, from the user interface (UI) to application programming interfaces (APIs) to the database.

    With today’s digital revolution, software content and value has grown exponentially. Smart and interconnected products are enabled by millions of lines of code. Businesses must accelerate their digital transformation to remain competitive and relevant, while continuing to support legacy platforms. The ever-growing scale and breadth of testing required for digital products is increasing complexity and thus time-to-market. By using artificial intelligence and analytics to automate testing, Eggplant enables improved software application development velocity and enhanced quality, driving competitive advantage and differentiation. The company’s customers span a wide range of sectors overlapping Keysight’s existing customer base while expanding software test opportunities into new end-markets.

    The combination of Keysight and Eggplant brings together two complementary companies to create an innovative force in the automated software test market across the physical and protocol layers and into the application layers. The acquisition enables bi-directional leverage of measurement technologies between both companies, resulting in increased solution differentiation in the expanded offering.

    “As a recognized leader and trusted advisor in layer 1-7 design and test, Keysight is excited to add Eggplant’s test capabilities for the software application layer, aligning with our strategy to grow our first-to-market software-centric solutions,” said Ron Nersesian, Keysight chairman and CEO. “We’re thrilled to welcome the Eggplant team to the Keysight family and look forward to working together in the fast-growing intelligent software test market with differentiated software-as-a-service technologies.”

    “Joining forces with Keysight gives Eggplant the ability to scale our intelligent automation platform and reach more organizations across the globe,” said Dr. John Bates, CEO of Eggplant. “We share a vision to accelerate innovation and together we will be able to help customers on their digital transformation journey. We’re proud of what we’ve accomplished through our employees and partners’ contributions, and we’re excited about this next chapter.”

    This transaction is valued at US $330 million. Eggplant had 2019 revenue of US $38 million and its CEO, John Bates, will join the Keysight leadership team reporting to Soon-Chai Gooi, president of Keysight’s Electronic Industrial Solutions Group.

    About Eggplant

    Eggplant, the fastest growing automation provider and, until now, a portfolio company of The Carlyle Group, provides user-centric, intelligent testing and performance solutions designed to optimize the digital experience, delight customers, and drive business success. Only Eggplant enables organizations to test, monitor, analyze, and report on the quality and responsiveness of software applications across different interfaces, platforms, browsers, and devices, including mobile, IoT, desktop, and mainframe. Learn more at Eggplant.

    About Keysight Technologies

    Keysight Technologies, Inc. (NYSE: KEYS) is a leading technology company that helps enterprises, service providers and governments accelerate innovation to connect and secure the world. Keysight’s solutions optimize networks and bring electronic products to market faster and at a lower cost with offerings from design simulation, to prototype validation, to manufacturing test, to optimization in networks and cloud environments. Customers span the worldwide communications ecosystem, aerospace and defense, automotive, energy, semiconductor and general electronics end markets. Keysight generated revenues of $4.3B in fiscal year 2019. More information is available at www.keysight.com.

    Additional information about Keysight Technologies is available in the newsroom at https://www.keysight.com/go/news and on Facebook, LinkedIn, Twitter and YouTube.

     

  • New Jupyter notebooks enable data scientists and policy makers to analyze real-time COVID-19 data

    New open source COVID-19 notebooks offer a toolkit that helps developers and data scientists answer important questions about the pandemic.

    By Frederick Reiss, Chief Architect, Center for Open Source Data and AI Technologies, IBM

    For data scientists and policy makers who are analyzing the effects of COVID-19 and trying to come up with actionable plans based on data, the information landscape is overwhelming. A near-constant flow of data from research studies, news outlets, social media, and health organizations make the task of analyzing data into useful action nearly impossible. Developers and data scientists need answers to their questions about data sources, tools, and how to draw meaningful and statistically valid conclusions from the ever-changing data.

    Policy makers face similar challenges. The United States has over 3,000 counties, each with a unique story of how COVID-19 is impacting its community. Policy makers are asking questions including: What stories can we tell in the aggregate? Are there patterns we see across the country? What regions or demographics are getting affected the most by the pandemic?

    Open source Jupyter notebooks offer actionable data

    To help, IBM’s Center for Open-Source Data and AI Technologies (CODAIT) created COVID notebooks, a toolkit that helps developers and data scientists answer important questions about the pandemic. We’ve taken care of the more mundane tasks, including:

    • Obtaining authoritative data on the current status of the outbreak
    • Cleaning up the most serious data-quality problems
    • Collating the data into a format amenable to easy analysis with tools like Pandas and Scikit-Learn
    • Building an initial set of example reports and graphs

    Taking care of these tasks frees developers and data scientists to focus on advanced analysis and modeling tasks instead of worrying about things like data formats and data cleaning. Our repository uses developer-friendly Jupyter notebooksto cover each of these initial data analysis steps.

    It’s important to note that the underlying data for COVID-19 changes on a daily basis. As you build your own analysis, you’ll want to update the results of your own notebooks frequently. But rerunning a collection of interconnected notebooks can be challenging. There are multiple stages of analysis, and the output of one step often feeds into multiple other steps. To simplify the process of updating your results with the latest data, we’ve created data processing pipelines using the Elyra Notebook Pipelines Visual Editorand KubeFlow Pipelines. For example, here’s the pipeline for county-level time series data for the United States:

    These pipelines let you rerun the entire extract-transform-analyze workflow with a single click of a button.

    Authoritative data sources

    The tools in our repository use authoritative sources to arrive at aggregate insights policy makers can use to make real-time, critical decisions. For county-level data about the United States, our data extraction notebook downloads the latest data from the COVID-19 Data Repositoryby the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. This data set is the primary source for many of the predictive models used by organizations working with the Centers for Disease Control.

    Our notebook fills in known gaps in this primary source with additional data from the New York Times Coronavirus (Covid-19) Data in the United Statesrepository (for more-complete data on Rhode Island and Utah) and New York newspaper THE CITY’s digestof the daily reports from the New York City Department of Health and Mental Hygiene (for borough-level data on New York City). Our notebooks use the European Centre for Disease Prevention and Control’s data on the geographic distribution of COVID-19 cases worldwideas their data source for worldwide data at the granularity of individual countries.

    The notebooks download all of these data sets as they run, for two reasons. First of all, these data sets change on a daily basis. Second, the license terms of the data sets do not allow commercial entities to redistribute the data. If you use our open source code in a commercial application, be sure to verify that you are staying within the bounds of the license terms for the underlying data!

    You can use our code as a jumping off point for more in-depth, interesting analysis. For example, you might want to analyze the data to find correlations between poverty level and infection rates. Open source developers and data scientists can easily build on these tools to extend the analysis to their individual use cases.

    Explanation of the technology used for developers and data scientists

    The notebooks in our repository are Jupyternotebooks. We use common Python data science libraries, including Pandas, Numpy, Matplotlib, seabornand scipy.optimize.

    Most of the cleaning and data analysis is done using Pandas dataframes, a framework that underlies a majority of analysis done by data scientists. IBM is also working to extend Pandas for natural language processing use cases. Parts of our COVID-19 time series analysis notebooks use the TensorArray extension typefrom our Text Extensions for Pandaslibrary to store time series tensors in the cells of Pandas dataframes. Our team also leveraged the graphical workflow editorthat we have built as part of the Elyra project to tie our notebooks into workflows that you can run each day as new data becomes available.

    Leveraging the open source tool and data set

    IBM and our team believe in the importance of democratizing technology, activating developers with the most up-to-date datasets and tools, which can help policy makers make the most informed decisions for citizens’ well-being.

    You can start contributing by using the repoto build analysis in your community, county and country. Developers and data scientists can also contribute directly to the tools that were used to do this analysis by making pull requests to our GitHub repository.

  • Puppet Introduces Public Beta of Relay for Cloud Native, Event-Driven Automation

    Relay listens to signals, triggers workflows and orchestrates hundreds of cloud providers, tools, and APIs

    PORTLAND, Ore., June 24, 2020 (GLOBE NEWSWIRE) — Puppet, the industry standard for infrastructure automation, today introduced the public beta availability of Relay, an event-driven automation platform. Relay automates processes across any cloud infrastructure, tools and APIs that developers, DevOps engineers, and SREs are managing manually today.

    The shift to the cloud has introduced immense complexity. DevOps engineers now oversee dozens of new tools and technologies, from containers and VMs to serverless applications and APIs, in addition to existing infrastructure. With the increasing number of tickets, monitoring alerts, and incident reports, engineers have reached a tipping point in productivity. Tool sprawl has created a “DevOps dumping ground” where engineers are either manually performing actions across disparate services or cobbling together homegrown solutions to help manage the influx of work. Relay relieves engineers of manual processes by listening to signals from disparate tools to automate workflows, freeing up time to focus on more valuable initiatives.

    “Without a way to manage and automate the flood of events and hundreds of APIs developers use – time, money and mental capital are being thrown away,” said Deepak Giridharagopal, CTO at Puppet. “Many engineers try to create their own one-off automation tools or integration hubs, but this is inefficient and risky. Relay replaces this home-grown digital duct tape with reusable, proven, automated workflows. It’s like IFTTT, but for DevOps.”

    Relay connects dozens of cloud platforms, tools, and APIs DevOps engineers already use, including PagerDuty, GitHub, Datadog, Jira, Terraform, Slack, and more. By listening to triggers from these services, Relay can address a wide variety of cloud automation use cases, including:

    • Security: Enforce security controls across your infrastructure, such as ensuring cloud storage buckets are secured, volumes are encrypted, or that unused SSH keys are removed from the account.
    • Cost Optimization: Proactively delete underutilized cloud resources across AWS, Azure, and GCP such as unattached volumes, unused load balancers, and untagged instances that contribute to expensive monthly bills.
    • Incident Response: When new incidents arise, Relay extends PagerDuty or VictorOps by automatically remediating known issues or running diagnostic actions with Bolt.
    • Continuous Delivery: Connect Relay to Docker Hub or GitHub to automatically provision cloud infrastructure with Terraform or Pulumi and deploy the latest versions of your microservices to Kubernetes, Google Cloud Run, AWS Lambda and other platforms.
    • Operations: Delete old snapshots, alert on quota usage, report on resource utilization, and more.

    “Today’s tools are focused on analyzing and responding to historical data. In contrast, Relay enables real-time reactions to events as they occur,” said Alex Bilmes, VP of Growth at Puppet. “Relay intelligently responds to external signals by combining event-based triggers with a powerful workflow engine in a single platform.”

    “Having worked in large scale data center environments and managing the complexities of modern-day hybrid cloud deployments, I have seen first hand how important it is to optimize for cost and efficiency with the rapid pace of technology adoption today,” said Stan Chan, Senior Software Engineer at Uber. “Relay delivers solutions for Enterprises to solve these problems and more. It delivers the promise of an automated future by enabling organizations to build smarter in order to solve real-world business processes in a modern, elegant, and effective way.”

    With the addition of Relay, Puppet provides market-leading offerings for task-driven, model-driven, and now event-driven automation across any infrastructure (on-premises or cloud). With Puppet, you increase productivity, eliminate mistakes and accelerate delivery by integrating with some of the most important enterprise infrastructure vendors like Hashicorp, ServiceNow, Atlassian, Splunk and many more.

    “With the rise of DevOps, the portfolio of tools aimed at addressing developer and operator needs has exploded over the last decade,” said Stephen O’Grady, Principal Analyst with RedMonk. “This has led, in turn, to rampant fragmentation and highly complex, disjointed workflows. Organizations that prize velocity, therefore, are beginning to rethink their developer experience and looking for ways to automate and integrate the different touchpoints. This is the opportunity that Relay is built for.”

    To learn more about Relay, please visit our website.

    Additional Resources

    About Puppet
    Puppet makes infrastructure actionable, scalable and intelligent. From the data center to the cloud, Puppet helps enterprises modernize and manage their infrastructure to deliver innovation and efficiency through continuous automation. More than 40,000 organizations — including more than 80 percent of the Global 5000 — have benefited from Puppet’s open source and commercial solutions to ensure business continuity, optimize costs, boost compliance and ensure security – all while accelerating the adoption of DevOps practices and delivery of self-service. Headquartered in Portland, Oregon, Puppet is a privately held company with offices in London, Belfast, Singapore and Sydney. Learn more at puppet.com.

  • SQream Raises $39.4 Million in Series B+ Financing Round to Accelerate its Deep Tech Product Innovation for the Rapid Analytics of Massive Data Stores

    New York, NY, JUNE 24, 2020 – SQream, provider of a data analytics acceleration platform that enables enterprises to gain insights from their massive data stores, announced today that it has raised $39.4 million in Series B+ funding. The round was led by Mangrove Capital Partners and Schusterman Family Investments, who joined existing investors, including Hanaco Venture Capital, Sistema.vc, World Trade Center Ventures, Blumberg Capital, Silvertech Ventures and Alibaba Group (NYSE: BABA). The proceeds of the investment will be used to recruit top talent to accelerate advancements in the Company’s technology and cloud innovation, and customer delivery platforms to better serve worldwide demand.

    While the COVID-19 pandemic created a major challenge for some companies, SQream’s growth is accelerating and the Company chose to raise funds from top new investors, and to add veteran investor Charlie Federman from Silvertech Ventures, and leading venture capitalist Roy Saar from Mangrove Capital Partners to its board of directors. This round of financing further underscores both the company’s existing and new investors’ confidence in SQream, and the team’s ability to deliver business critical innovation to leading global enterprises.

    “I have watched with enthusiasm the-product innovation and resulting customer enthusiasm that SQream has earned from large global customers,” said Charlie Federman, Partner at Silvertech Ventures. “I am excited to be joining SQream’s board of directors at this critical time in its growth into the cloud, and believe that SQream will be a really important vendor for enterprises that need help getting timely critical insights from their data.”

    SQream’s data acceleration platform enables the fastest available analysis of hundreds of terabytes to petabytes of raw data from Hadoop or other data warehouses and databases. With SQream in their data pipeline, enterprises gain unrestricted access to their data, and are free to focus on uncovering new business powering insights.

    “Given the growth of data, which has been on a rocket-ship trajectory to zettabyte levels due to rapid digitalization, we are just scraping the surface of how companies will be generating value from their data,” said Roy Saar, Partner at Mangrove Capital Partners. “We are impressed by the quality and experience of the management team. We also share the conviction in SQream’s important role in driving business performance and providing a competitive edge. I could not be more excited about SQream’s potential for making a significant impact in the market.”

    “From the very beginning, our vision has been to enable enterprises to analyze more of their data much faster, providing better, more accurate and comprehensive business insights,” said Ami Gal, CEO and co-founder of SQream. “We have made our vision a reality by continuously improving our product, and by investing in top developers who enjoy the challenge and motivation of deep tech product innovation.”

    “The commitment we have received from both new and existing investors in this round, and from our customers, even during such a challenging period, is a strong validation of our vision and market potential,” noted Gal. “We look forward to working together to accelerate our expansion, and to further empower enterprises across the globe with timely and impactful business insights.”

     

    About SQream Technologies

    SQream develops and markets SQream DB, designed to obtain unparalleled business intelligence from massive data stores. Global enterprises use SQream DB to analyze more data than ever before, while achieving improved performance, reduced footprint, significant cost savings and the ability to scale the amount of data they analyze to hundreds of terabytes and more. SQream DB is available both on premise and in the cloud. To learn more, visit sqream.com or follow us on twitter @sqreamtech.