Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The rapid adoption of AI coding assistants is transforming software development in ways both good and bad. Developers can produce more code faster than ever with AI, and 96% of developers report using AI coding assistants to streamline their work. AI code generation is becoming mainstream, and in late 2024, Google reported that AI writes more than 25% of its code. While GenAI tools increase productivity, they’re also creating more work for application security teams.

I'm thrilled to announce Snyk API & Web, our next-generation dynamic application security testing (DAST) solution. It's more than just a product launch; it's Snyk’s answer to securing the complex, AI-powered applications developers are building today, deepening the integration of DAST into our comprehensive Developer Security Platform.

Over the past several days, the cybersecurity community has watched closely as uncertainty swirled around the future of the MITRE-run CVE (Common Vulnerabilities and Exposures) program following a letter to its board of directors that its federal funding could abruptly end. As of this blog posting, news outlets like Reuters are reporting that a last-minute extension has been granted, providing temporary relief.

The Latin American technology landscape is growing rapidly, with organizations embracing digital transformation and cloud-native development. This growth brings incredible opportunities to this market – but can also introduce significant security challenges that can slow innovation if not addressed proactively.

Developers worldwide continue to adopt AI to speed up software delivery. But this speed often impacts security, as developers assume AI-generated code is secure without thoroughly checking for errors or vulnerabilities.

Today, I'm absolutely thrilled to announce our newest innovation in the security space: our new CLI tool, Greybeard. After years of giving polite, professional security advice, we've realized what developers really need is an ornery virtual security expert who tells it like it is.

After implementing a DevSecOps strategy from the ground up — including secure design, testing and monitoring, and risk-based remediation — you will need to focus on analysis and governance. After all, organizations need to regularly measure and refine their security processes to mature their DevSecOps programs.

Data poisoning is a sophisticated adversarial attack designed to manipulate the information used in training artificial intelligence (AI) models. By injecting deceptive or corrupt data, attackers can hurt model performance, introduce biases, or even create security vulnerabilities. As AI models increasingly power critical applications in cybersecurity, healthcare, finance, and many other industries, maintaining the integrity of their training data is absolutely critical.