News and Tech Update

ProjectDiscovery has made available an autonomous security testing platform that leverages an open source artificial intelligence (AI) testing framework to detect and validate vulnerabilities at a lower total cost. Company CEO Rishi Sharma said version 1.0 of the Neo platform is also available via a cloud service that makes it possible for DevSecOps teams to run tests and conduct investigations using a consumption-based pricing model. Additionally, Neo 1.0 adds integrations with DevOps tools and platforms such as GitHub, Jira, Confluence, Slack, Linear, application programming interfaces (APIs), webhooks, and the Model Context Protocol (MCP). Based on an open source project, ProjectDiscovery, in addition to using Neo to detect, validate and route vulnerabilities to the responsible developer, also provides access to Nuclei, an open-source vulnerability scanner driven by customizable YAML templates to detect misconfigurations and exploits. Also included are tools such as Subfinder fo...

Every enterprise deployment conversation eventually comes down to the same question: Which strategy is right for this release? Most teams answer it by reaching for whatever approach they used last time. That is not a strategy; it is a habit, and habits developed for one deployment context carry real risk when applied uncritically to a different one. The deployment strategy decision matters more than most teams give it credit for. Get it right and releases become routine — predictable, reversible and low-drama. Get it wrong and a deployment that should have been straightforward becomes a Friday afternoon incident investigation. This guide walks through the four most widely used software deployment strategies, identifies the scenarios each handles best and provides a decision framework for matching the right approach to the right situation. The Four Strategies and What They Are Actually For Before getting into scenarios, it helps to be precise about what each strategy does and does...

Code creation has never been faster, but the machinery around it has not sped up in step. AI coding tools can now produce more lines in a morning than a team used to ship in a sprint, while testing pipelines, review processes and device coverage still move at the pace they did two years ago. The result is a widening gap between how much software is being written and how much of it anyone can actually verify — and that gap is where production incidents live. Mike Vizard sits down with Prince Kohli, CEO of Sauce Labs, to work through what that imbalance is doing to release quality. Kohli’s framing is that leaders keep mistaking code velocity for product velocity. Generating an application in an afternoon is not the same thing as shipping one. Reviews still need to happen, tests still need to be authored, device coverage still needs to be real, user journeys still need production-like validation. Skip those layers and the AI advantage evaporates the first time a subtle defect make...

A survey of 400 business and engineering executives finds 80% have traced a production incident, outage, or customer-impacting defect to code generated by artificial intelligence (AI) tools in the past 12 months. Conducted by Wakefield Research on behalf of Sauce Labs, a provider of an application testing platform, the survey finds 83% of respondents work for organizations where more than 10% of the code running in production environments has been generated using AI, with 28% now running more than a quarter of the code in their production environments using AI tools. A full 93% said they also receive reports on that code, but only 38% receive them regularly. A total of 89% also said they disclose to customers when code has been generated using AI, but only 27% said they do so consistently. A total of 70% said their organizations now spend more than $1 million annually on AI-assisted application development tools and platforms, with 41% spending $5 million or more. A total of 89% de...

Ask any engineering team whether they have test coverage, and most will say yes. Ask them whether they trust what that coverage is actually telling them before a deployment, and the conversation gets more complicated. The gap between having tests and having visibility into what those tests mean is where most deployment anxiety lives. Teams with extensive automated test suites still pause before major releases. Teams with high coverage percentages still experience production incidents from regressions that should have been caught. The tests exist. The confidence does not always follow. Test management tools are supposed to close that gap. In practice, how much they close it depends almost entirely on how they are used — and most teams are using them for the wrong thing. The Difference Between Tracking Tests and Understanding Coverage The most common use of test management tools is organizing and tracking test cases. Which tests exist, which ones ran, which ones passed, which ones f...

For years, DevOps had a pretty straightforward mission: help teams ship reliable software faster by getting development and operations folks working together. Tools like automation, continuous integration, continuous delivery, infrastructure as code, and better observability have all been key pieces of the puzzle. But now, things are shifting again. This time, artificial intelligence is changing the whole landscape. AI isn’t just some fancy code-completion tool anymore. Modern engineering teams are building it into almost every stage of how they deliver software. From writing deployment pipelines, to troubleshooting incidents, and analyzing data, AI is stepping into each corner. People are starting to call this new wave “AI-Native DevOps.” In this world, AI’s not just here for a productivity boost. It’s actively involved in pushing software out the door. The change might sound subtle, but it’s actually a pretty big deal. Old-school DevOps relied on ...

Standardized developer environments promise to eliminate one of software engineering’s oldest explanations: “It works on my machine.” By packaging approved runtimes, dependencies and tools into containers, virtual machines or cloud workspaces, organizations expect every developer to start from the same foundation. The approach delivers clear benefits. It reduces onboarding time, minimizes dependency conflicts and makes development environments easier to reproduce. Yet developers using the same environment definition can still experience different build times, test results, network behavior or access failures. Local results may also differ from CI and production, despite sharing the same container image. A container defines only part of the development environment. The systems supporting it continue to influence how software is built, tested and executed, making reproducibility a broader engineering problem than environment standardization alone can solve. The Envi...