Did you miss a session from MetaBeat 2022? Head over to the on-demand library for all of our featured sessions here. Overcoming the challenges of securing devops and software supply chains from malicious, unpredictable attacks with new technologies dominates Gartner's latest Hype Cycle for Application Security. One of the most concerning insights this year's hype cycle shed light on is that no single application security innovation can deliver comprehensive security. In light of this, CISOs are also forcing the consolidation of their tech stacks to improve their teams' efficiency at identifying risks while reducing costs.

The considerable number of articles cover machine learning for cybersecurity and the ability to protect us from cyberattacks. Still, it's important to scrutinize how actually Artificial Intelligence (AI),Machine Learning (ML),and Deep Learning (DL) can help in cybersecurity right now, and what this hype is all about. First of all, I have to disappoint you. Unfortunately, machine learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where machine learning is thriving. There will always be a man trying to find weaknesses in systems or ML algorithms and to bypass security mechanisms. What's worse, now hackers are able to use machine learning to carry out all their nefarious endeavors.

One of the most amazing dynamics within the DevOps enterprise community is seeing business leaders co-presenting success stories with their technology leadership counterparts. For example, Ken Kennedy (executive vice president and president for Technology and Product at CSG) and Kimberly Johnson (chief operating officer at Fannie Mae) described the achievements of their technology leadership counterparts and why it was important to them. I expect this trend to continue, especially given how COVID-19 has accelerated the rate of digital disruption. I believe this bodes well for all of technology. With the rise of hybrid (remote/in-office) product teams, upskilling and online training initiatives will expand.

CollabNet VersionOne, XebiaLabs, and Arxan Technologies today announced their combination and strategic transformation to Digital.ai, a new software company that brings together leaders in business agility, software delivery, and application security into one integrated, intelligent value stream platform. Backed by TPG Capital, Digital.ai is on a mission to revolutionize how enterprises create, measure, deliver, secure, and continuously improve digital products that provide value, fuel revenue growth, and enable innovation in today's rapidly changing world. The Digital.ai Value Stream Platform seamlessly integrates all the disparate tools and processes across value streams, uses data and AI/ML to create connective tissue between them, and provides enterprises with the real-time, contextual insights they need to drive and sustain their digital transformation and produce great business outcomes. By streamlining processes across teams and providing continuous feedback loops throughout the development lifecycle, organizations can focus on what matters most to drive efficiencies, reduce costs, and create meaningful value for customers. "In these challenging times, your digital presence is your business. Digital.ai enables enterprises to focus on business outcomes instead of outputs, unifying value creation, delivery, and protection practices to drive efficiencies and create engaging, secure digital experiences that customers value and trust," said Ashok Reddy, CEO of Digital.ai.

Plano, TX - April 15, 2020 - CollabNet VersionOne, XebiaLabs, and Arxan Technologies today announced their combination and strategic transformation to Digital.ai, a new software company that brings together leaders in business agility, software delivery, and application security into one integrated, intelligent value stream platform. Backed by TPG Capital, Digital.ai is on a mission to revolutionize how enterprises create, measure, deliver, secure, and continuously improve digital products that provide value, fuel revenue growth, and enable innovation in today's rapidly changing world. The Digital.ai Value Stream Platform seamlessly integrates all the disparate tools and processes across value streams, uses data and AI/ML to create connective tissue between them, and provides enterprises with the real-time, contextual insights they need to drive and sustain their digital transformation and produce great business outcomes. By streamlining processes across teams and providing continuous feedback loops throughout the development lifecycle, organizations can focus on what matters most to drive efficiencies, reduce costs, and create meaningful value for customers. "In these challenging times, your digital presence is your business. Digital.ai enables enterprises to focus on business outcomes instead of outputs, unifying value creation, delivery, and protection practices to drive efficiencies and create engaging, secure digital experiences that customers value and trust," said Ashok Reddy, CEO of Digital.ai.

Snyk, a cybersecurity platform that helps developers find vulnerabilities in their open source applications, has raised $150 million in a round of funding led by New York-based private equity firm Stripes, with participation from Salesforce Ventures, Coatue, Tiger Global, BoldStart, Trend Forward, and Amity. This takes Snyk's total funding to $250 million from backers including Alphabet's GV and Accel, including a $22 million series B round in 2018 and a $70 million follow-on round just a few months ago. A Snyk spokesperson said that the company is now worth more than $1 billion, which is at least double the $500 million it was valued at back in September. Founded in 2015, London-based Snyk targets developers -- rather than cybersecurity personnel -- to help them find and fix flaws in their source code, as well as their containers and Kubernetes applications. The developer connects Snyk to a code repository in the likes of GitHub, GitLab, or Bitbucket, and Snyk then scans for vulnerabilities (or license violations), providing a description of the problem, noting where the flaw lies in the code, issuing a severity rating, and even suggesting a fix.

When software is at the core of your business and your customers present a high-profile target for cybercrime, application security becomes a driving factor. IGT's products and solutions enable players to experience their favourite games across all channels and regulated segments. But innovating to stay ahead of changing player requirements means new code is continually being turned out – by thousands of developers. The challenge was how IGT would meet business needs for speed-to-market while maintaining thorough application security throughout the development process. By employing the help of IBM Business Partner HCL, and the machine learning and AI-based capabilities of IBM Security AppScan, IGT can now rule out hundreds of'false positive' security issues, enabling them to focus on critical sections of code that really matter.

Artificial intelligence (AI) and machine learning (ML) are some of the latest tools being used in the fight against application security vulnerabilities. However, the complexities involved can make it hard to discern what's actually being used and what lives in a fictional Hollywood setting. I spoke to Ilia Kolochenko, CEO of web security company High-Tech Bridge to clear up any confusion. Scott Matteson: What is the overall state of application security today? Has it improved in the last 12 months?

The considerable number of articles cover Machine Learning for cybersecurity and the ability to protect us from cyber attacks. Still, it's important to scrutinize how actually Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can help in cybersecurity right now and what this hype is all about. First of all, I have to disappoint you. Unfortunately, Machine Learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where Machine Learning is thriving. There will always be a man trying to find weaknesses in systems or ML algorithms and to bypass security mechanisms. What's worse, now hackers are able to use Machine Learning to carry out all their nefarious endeavors. Fortunately, Machine Learning can aid in solving the most common tasks including regression, prediction, and classification.

Gil: ML technologies examine similarities found in different types of web traffic. They collect all sorts of information that taken together allow for the classifier to decide which traffic is valid and which traffic may be a threat. The techniques we use must also have a very strong feedback loop, so the platform becomes better as you are using it. Once you identify the corner cases, for example, you should not need to identify any more. The machine will then know what to do.