77% of organisations say AI adoption is outpacing governance, proving that the issue isn't a policy gap, it's a visibility one.

YouTube video by TestinGil - Gil Zilberfeld

The widespread use of #AIcoding tools is causing a #crisis among #programmers, who are becoming overly reliant on these tools and losing their ability to write code by hand. This #overreliance is leading to a decrease in #criticalthinking and #problemsolving skills, resulting in a rise in #errors and #security issues in #AIgeneratedcode. The situation is causing frustration among programmers, who feel their expertise is being devalued. https://futurism.com/artificial-intelligence/software-engineer-ai-chaos-press-enter-all-day?eicker.news #tech #news #ainews

AI-generated code is accelerating software development, but it is also creating risk at machine speed that many application security programs are not built to handle. Organizations must govern AI-assisted code with automated testing, dependency controls, remediation, and policy enforcement before it reaches production. #Veracode #AIgeneratedcode #CISO

A Cloudflare blog post claiming a "production-grade" Matrix homeserver on Workers didn't survive community scrutiny. Missing federation, incomplete encryption, and TODO comments in authentication logi...

AI generated code is changing how we think about speed, quality, and cost—but new research shows it may be creating hidden productivity losses and technical debt for engineering teams.

Phantom APIs are now emerging through AI-generated code, creating hidden attack surfaces. Learn how they form and how to detect them before attackers do.

If software developers want to benefit from AI-generated code tools, they must mitigate some of the risks they could bring first, Synopsys’ Lucas von Stockhausen said at Infosecurity Europe

AI-generated code verification startup Qodo raises $70M - SiliconANGLE

Vibe-coded apps that make it to production can be a minefield for security teams. Here are key takeaways for your AppSec team. more

The article discusses the legal and ethical challenges posed by AI-generated code in open-source software development. It highlights how AI tools produce code with uncertain copyright protections, threatening the integrity of open-source projects that rely on copyright enforcement. Contributors warn that fully AI-generated code lacks copyright ownership, while partially AI-assisted code remains legally ambiguous. This creates risks for open-source communities, as low-quality or malicious code could pollute repositories. The text also mentions the potential for AI to automate code reviews, but cautions against merging AI-generated contributions without human oversight. Legal experts note that the absence of clear copyright frameworks for AI output could undermine the licensing models (like GPL) that govern open-source projects. The discussion underscores the need for gatekeeping and quality standards in projects facing an influx of AI-generated submissions, raising concerns about the long-term viability of open-source ecosystems in the age of AI. Contributors argue that while AI can enhance productivity, it also introduces complexities that require careful management to preserve the trust and collaboration central to open-source development.