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Vibe Coding Puts Cybersecurity Pros’ Skills to the Test
For developers, each year brings a fresh set of automation tools designed to help them create code faster and roll out application updates that meet ever-changing business needs. Now, artificial intelligence (AI) and large language models (LLMs) have ushered in another era in this development known as “vibe coding.” Vibe coding is an approach where a developer or, in many cases, an amateur coder, describes what they want to create in natural language, and an AI system generates, edits or debugs the code based on an ongoing set of instructions. This approach to application creation shifts some work from writing every line of code toward steering, reviewing and validating AI-generated output. This technique for creating applications remains in its infancy but has caught on, especially as less-experienced coders and developers can take an idea and create an app using various AI platforms. A study by Gartner found that 40% of new business software could be created with techniques involving
Speed vs. Risk: Experts Weigh In on Using AI Coding Assistants
Artificial Intelligence in software development has been framed around speed and efficiency. From automated code completion co-pilots to repositories generated via "vibe coding," AI coding assistants have altered the mechanics of writing software. As these tools transition from novelty plugins to integrated engineering tools, a stark reality is coming to light: the gains made by generating a hundred lines of code in seconds can quickly be erased if that code introduces security flaws and architectural drift. How do the rules of engagement change when shifting from modern "greenfield" projects to legacy systems? At what point does auditing AI-generated "slop" begin to outweigh the ROI of writing it manually? To map these boundaries, we spoke with technology leaders and engineering executives to unpack the hidden liabilities, shifting responsibilities and the exact tipping points where AI coding assistants become a liability rather than an asset. At what point does a project’s complexity
How IT Leaders Can Rebuild Trust in AI-Generated Code
AI is helping software teams produce code faster than ever, but the gains in productivity are creating a new challenge for IT leaders; maintaining confidence in what reaches production. A Tricentis survey of more than 2,500 CEOs, CIOs, CTOs, engineering leaders, DevOps professionals, QA teams and developers found 60% of organizations are deploying untested code into production environments. While that figure is largely unchanged from the previous year, the reasoning behind it has shifted; in 2025, organizations primarily attributed quality failures to accidental oversights. Today, many acknowledge they are knowingly accepting the risk. Leadership pressure to prioritize speed was cited by 32% of respondents, while 30% said the volume of AI-generated code has become too overwhelming to test completely. The findings suggest a growing tension inside software organizations--AI is accelerating development cycles and enabling teams to deliver features more quickly, but testing, governance and
Skills Visibility Gap Is Holding Back Hiring, Retention Efforts
Many organizations are struggling to identify and deploy the skills they already have, creating a growing gap between available talent and how it is used, according to a report from TalentLMS. Half of employees and managers surveyed said their company hires externally for skills that already exist in-house, pointing to a widespread breakdown in skills visibility. The report, based on responses from more than 1,500 U.S. employees and managers, highlights a shift in workforce challenges. Rather than a lack of talent, many organizations are dealing with “invisible talent”—skills that exist but are not recognized, tracked or effectively applied. Only a small minority of respondents said their organization does not face issues with skills visibility. That gap shows up in how managers and employees view workforce capabilities. While 90% of managers said they understand their teams’ skills, only 69% of employees agreed. A similar disconnect exists around development: 90% of managers said they
Review First, Execute Later: Use AI Coding Tools Without Losing Your Edge
In the development lifecycle, the software engineer is evolving from a traditional code author to a pseudo system architect and intent director thanks to AI. The widespread adoption of Large Language Models (LLMs) in IDEs has accelerated how quickly deliverables are shipped, but has also introduced challenges to software creation. When code can be written in seconds, concepts like ownership, technical debt and accountability are often as confusing as AI’s hallucinations. Writing software has never been just about syntax. It’s also about understanding logic, predicting edge cases, and understanding true human behavioral use patterns. To help make sense of it all, we gathered insights from industry experts on integrating generative AI into your workflow responsibly, ensuring you leverage the power of automation without compromising on security, licensing or personal engineering accountability. How should developers define "original work" today? Is there a professional obligation to discl