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Software companies are constantly trying to add more and more AI features to their platforms, and it can be hard to keep up with it all. We’ve written this roundup to share updates from 10 notable companies that have recently enhanced their products with AI.  JFrog launches end-to-end DevSecOps platform for deploying AI applications JFrog is releasing a new end-to-end solution for developing and deploying enterprise AI applications that brings together development teams, data scientists, and machine learning engineers into a single platform.  JFrog ML provides a holistic view of the entire AI software supply chain, from software packages to LLMs, so that companies can ensure their AI applications are secured in the same way their traditional software is.  It provides security scanning for AI models, whether they were created in-house or are from a third-party. Other key features include providing a single system of record, reproducible artifacts for all models crea...

I’m always looking for more time and space to get things done. For every useful unit of actual hands-on-keys work time I spend writing thought leadership pieces, or time on briefing and advisory calls with innovative vendors, there must be a corresponding amount of time away from the computer to realign my thinking and grasp the connections between technology categories and their value to end customers. As humans, we never expected to be as fully engaged as we are now, with per-worker productivity at an all time high, a pocket supercomputer that constantly nags us for attention wherever we go, and some AI coming in to scoop up our ‘think time’ by repeating our collective thoughts back at us. But I wouldn’t let that short-change my non-AI-generated thoughts on the impact of AI on software development, as I’m constantly analyzing this topic. AI is driving the tool chain While we find vendor claims of “AI-driven” software development everywhere we look, there are many different flavor...

Andrew G. Barto and Richard S. Sutton have been named as the recipients of the 2024 ACM A.M. Turing Award for their contributions to the field of reinforcement learning beginning in the 1980s. Reinforcement learning is a training method for AI systems that teaches them how to make the most optimal decisions through a series of signals known as rewards. ChatGPT, for instance, was trained using a method called reinforcement learning from human feedback (RLHF).  They wrote the textbook “Reinforcement Learning: An Introduction” in 1998, and it is still a standard reference in the field, having been cited over 75,000 times.  Barto and Sutton were responsible for developing many of the basic algorithmic approaches used in reinforcement learning, including temporal difference learning, policy-gradient methods, and using neural networks to represent learned functions.  Their work has also led to discoveries in the neuroscience field, specifically that certain reinforcement...

GitHub announced it is making some changes to GitHub Advanced Security (GHAS) , its AI-powered solution for application security that offers remediation, static analysis, secret scanning, and software composition analysis. Beginning April 1, GHAS will be split into two products that will be available as standalone options. GitHub Secret Protection prevents secret leaks by scanning secrets and utilizing AI-powered detection. GitHub Code Security identifies and remediates vulnerabilities in code via code scanning, Copilot Autofix, security campaigns, and Dependency Review Action.  GitHub Secret Protection will be available for $19/month/active committer and GitHub Code Security will be available for $30/month/active committer.  Currently, GitHub Advanced Security is only available to purchase as an add-on for GitHub Enterprise customers, which is the company’s highest tier plan that starts at $21/month/user. With these changes, any GitHub Team subscriber (starting at $4/mo...

Sonatype ®, the leader in software supply chain security, today announced end-to-end  AI Software Composition Analysis (AI SCA)  capabilities that enable enterprises to harness the full potential of AI. With its unparalleled expertise in open source governance, Sonatype now extends its trusted platform to protect, manage, and optimize AI/ML models across development and deployment. Sonatype is the first and only company providing an end-to-end AI SCA solution, ensuring that enterprises can adopt AI with the same level of safety and productivity as traditional open source. Open source AI/ML adoption is soaring — over the last 12 months, Sonatype has identified more than 300,000 models within customer software supply chains. As organizations rush to integrate AI-powered software and agentic AI solutions, they face the same security, compliance, and governance challenges that once plagued open-source software adoption. To confidently manage open source AI/ML usage in software su...

JFrog is releasing a new end-to-end solution for developing and deploying enterprise AI applications that brings together development teams, data scientists, and machine learning engineers into a single platform.  JFrog ML provides a holistic view of the entire AI software supply chain, from software packages to LLMs, so that companies can ensure their AI applications are secured in the same way their traditional software is.  It provides security scanning for AI models, whether they were created in-house or are from a third-party. Other key features include providing a single system of record, reproducible artifacts for all models created in the platform, simplified model development and deployment processes, and dataset management and feature store support.  “As the demand for AI-powered applications continues to grow rapidly, so do the concerns around the ability to control and manage this new domain on all fronts – from MLOps to ML security. In fact, our own team...

Hybrid work structures have become a popular choice for many modern companies. Not only do they add more flexibility when scaling a business, but they also help to create a better work-life balance for all employees. However, while maintaining a decentralized workforce does come with a number of advantages, there are also certain risks associated with this format. Many of these risks are security-related and can lead to significant issues if they aren’t properly addressed. Common Security Threats That Decentralized Teams Face Having a decentralized workforce is a much more common business scenario than it was 10 years ago. As technology has continued to advance over the years, there are now more ways for organizations to stay productive while being supported by employees spread across the globe. However, this shift has also created a unique set of security challenges that organizations need to address. Maintaining network integrity and ensuring data security and compliance has be...

CData Software , a leading provider of data connectivity solutions, today announced the expansion of its collaboration with Google Cloud, further extending data access capabilities for Google Cloud users. “We’re thrilled to expand our strategic partnership with Google Cloud. Meeting the growing data connectivity needs of modern enterprises is one of the biggest challenges facing enterprise software providers. To help customers address this pain point, we’ve built out our embedded data connectivity solutions to ensure they provide unmatched breadth and depth of data access while also providing fast, easy deployment within our customers’ products,” said Manish Patel, CPO at CData. “Through our embedded data connectors, customers across the Google Cloud portfolio will be able to leverage flexible data connectivity to access a wider variety of data sources directly within their Google Cloud environment.” “Based on years of successful collaboration with Google Cloud, we’ve chosen to expan...

Teradata , a provider of data analytics solutions, today announced a new database offering for managing vector data. Teradata Enterprise Vector Store manages unstructured data in multi-modal formats like text, video, images, and PDFs. It can process billions of vectors and integrate them into pre-existing systems, and offers response times in the tens of milliseconds.  According to the company, vector stores are an important foundation for agentic AI, but many vector stores require organizations to make tradeoffs, such as getting fast results, but only for small data sets, or being able to handle large vector volumes, but not at the speed required by agentic AI use cases.  “Vector stores are at the root of how we bind truth to generative AI models and agentic AI. They are essential to any data management practice, but their impact is limited when they are slow or siloed,” said Louis Landry, CTO of Teradata. “Teradata’s long-standing expertise in high concurrency and linear ...