CA’s BTCS2.0: Change Is The New Normal
Jun07

CA’s BTCS2.0: Change Is The New Normal

SANTA CLARA, Calif.: Determined to be the leading toolmaker for the software-enabled, data-driven, digital transformation business phenomenon that is reshaping the world, CA Technologies held its second annual Built to Change Summit to update analysts and journalists on where it is, and where it wants to go. At last year’s BTCS the venerable software developer (1976) detailed how it was transitioning from a $4-billion legacy software giant with relatively flat sales into a more agile and fast-growing DevSecOps vendor for the the rapidly emerging DT world. Fast-forward 11 months and the company reported quarterly and annual revenue increases to $1.083 billion and $4.235 billion, respectively, and is forecasting relatively flat growth for the next quarter. In addition to its financials, CA also announced it would be laying off 800 (out of 11,000 employees) in restructuring, and adding another 500-600 staff with ‘different skills’. The company needs fewer employees with skills related to “legacy platforms” and more with skills related to software as a service, said CEO Michael Gregoire. While the company’s roots are in the mainframe, which is undergoing something of a renaissance, it is DevOps and more specifically DevSecOps where it’s future lies.Depending upon your source, DevOps is a flourishing market, especially in the enterprise. Forrester Research declared 2017 to be the year of DevOps with 50% of organizations implementing it, and 2018 will be the year of enterprise DevOps. ‘DevOps has reached “Escape Velocity”’, noted Principal Analyst Robert Stroud, with momentum occurring within all industry sectors but with healthcare, banking, insurance and manufacturing sectors leading the charge. In addition to forecasting a global DevOps software market in excess of $5.6 billion by 2021, IDC offered some interesting predictions that should sit well with CA’s DevSecOps ambitions, including: -cognitive computing, artificial intelligence, and machine learning will become the fastest growing segments of software development by the end of 2018; by 2021, 90% of organizations will be incorporating cognitive/AI and machine learning into new enterprise apps -by 2019, over 70% of routine development-lifecycle tasks will be automated, supported by AI fed from existing data streams, with an agile DevOps pipeline driving and incubating lifecycle and application development intelligence; -by 2021, over 50% of CIOs will have appointed heads of delivery; integrated their dev, PMO, and ops groups; reduced silos; expanded their DevOps practices; and implemented shift-left testing to accelerate innovation; and, -development without integrated security and compliance will fail; progressive orgs have prioritized security due to uptime and compliance concerns, accelerating the need for agility and a curated OSS-dev portfolio. Security-led development will be a priority for 90% of orgs by 2020. 451 Research (together with security software testing...

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Cloud Computing Security Chaos Continues at RSA Conference 2018

My esteemed colleague, Doug Cahill, did a great job at the RSA Conference with a killer presentation on hybrid cloud security. Unfortunately, Doug’s presentation occurred on Thursday afternoon, when many conference attendees were catching flights home, packing up their booths, or recovering at a bar somewhere else in San Francisco. Despite the timing, about 150 souls showed up, but I’m guessing that Doug’s conference room would have been overflowing if his presentation was on Tuesday rather than Thursday. As I wrote in a recent blog, it was important to focus on cloud security at RSA 2018. Why? Organizations are rapidly adopting hybrid clouds with DevOps leading the charge. This places a double whammy on security teams that have little cloud computing experience and a limited relationship with DevOps teams. Since Doug gave a stellar performance in explaining the problems and potential solutions to cloud security, allow me to provide a few highlights from his presentation: To read the complete article, CLICK...

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…Addressing the Hybrid Cloud Security Readiness Gap

Over the last few months, some established cybersecurity brands have made strategic moves while emerging market leaders have announced compelling capabilities and initiatives. This notable level of industry activity is indicative of an acceleration of market maturity driven by a cloud security readiness gap. That is, most IT and cybersecurity teams are catching up to secure the cloud services, applications, and infrastructure, their organization is already using, and to do so, they are retooling their processes, policies, skills, and technologies. Click here to read the rest of this blog on CSHub.com. To read the complete article, CLICK...

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Where Endpoint Management and Security Meet

Upgrade existing tools or net new platform investments? This is the question IT operations and information security teams are wrestling with as they attempt to secure an expanding perimeter driven by cloud, mobile, and IoT. Should companies maintain a traditional siloed tool approach or embrace a modern management approach that unifies management and security policies across users, devices, applications, networks, and data? The ultimate goal is to deliver a secure workspace by authenticating users across devices and enforcing policies based on location, device type, application, data, and the security posture of the end-user. This seems simple enough, but given the stress mobility, cloud, and IoT are putting on IT and security pros and the market dynamics ESG is observing with endpoint management and security vendors, business are finding themselves in a quandary. The one constant for businesses is change as more devices, applications, and innovative ideas continue to pour in, but these leave IT operations and security teams with the challenge of answering: To read the complete article, CLICK...

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Artificial Intelligence and Machine Learning…

The traction over the last few years in the artificial intelligence (AI) and machine learning (ML) space is remarkable, and I’m not just talking about consumer-based products like self-driving cars, or virtual assistants like Google Assistant, Alexa, or Siri. While those products get the headlines, AI/ML is rapidly spreading across the enterprise IT space. I feel like I can’t go a day without a company mentioning AI or ML as part of their product or forward-looking strategy. It’s not just for crazy, sci-fi predictive analytics projects in a bunker somewhere. While that definitely still happens, AI and ML (and deep learning too) are being used across all aspects of IT: big data, cloud, IoT, security, infrastructure, systems management, etc.While AI/ML is a top priority for businesses that expect it to have a significant (positive) business impact as they continue to digitally transform, investments remain modest because of its sheer impact on all aspects of the infrastructure. Challenges associated with infrastructure cost, lack of in-house expertise, and insufficient data quality are just the start. To read the complete article, CLICK...

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