The pace is not slowing down. Between May 18 and June 1, 2026, four distinct supply chain campaigns swept through npm, PyPI, Crates.io, GitHub Actions, and Composer.
Accelerating security solutions for small businesses Tagore offers strategic services to small businesses. A partnership that can scale Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market.
If the LLM hits a security constraint that’s in the way of delivering what you asked for, it will silently fail. It will go around that security constraint and hardcode secrets.
I recently wrote about how today’s cyber risk is defined less by breakthrough innovation and more by the industrialization of existing weaknesses. Given this, I wanted to dig a little deeper. Over a weekend I conducted some analysis on a longitudinal Aggregate Cyber Risk Index that scores six core threat vectors daily for 1,000 days on a 0–100 scale, drawing on six macro categories.
Every company has a version of the same thing. Sometimes it’s a security wiki. Sometimes it’s a Confluence page. Sometimes it’s a PDF nobody wants to update.
If Gen AI adoption were a drinking game, most companies would be three rounds in and still adding shots. I mean, with a new LLM-powered feature every sprint, agents wired into internal APIs, RAG pipelines indexing everything from Confluence to the HR drive, i.e., fast, exciting, and almost nobody checking what happens when someone hands the model a sentence or a txt.file it wasn’t supposed to receive.
Recently our CMO, Tony Thompson, caught up with Seemplicity co-founder and CPO, Ravid Circus, in Paris to talk about the massive shift in the cybersecurity landscape caused by Claude Mythos. As AI research models like Claude Mythos hyper-scale the ability to identify vulnerabilities and weaponize exploits in minutes rather than months, traditional risk-based vulnerability management must evolve. In this video, you will learn.
In this Feature Focus, Megan Horner, Product Marketing Director at Seemplicity, explores the evolving landscape of vulnerability management in the AI era. As the rise of AI models like Claude Mythos enables attackers to shrink exploit windows, security teams are facing an overwhelming flood of high-priority vulnerabilities.
For years, application security ran on a simple assumption: software moves through a lifecycle, and security inspects the artifacts as they travel from development to production. Developers plan, write code, commit it, test it, scan it, and ship it. Every control built, including pull request reviews, CI/CD gates, and post-commit scanning, assumed a human was sitting between each step, making decisions a tool could later check.
Security budgets have never been higher. The average enterprise now runs 50 security tools, and most teams added more last year than the year before. And yet, alert fatigue is at the breaking point. Coverage gaps in mobile and API environments continue to widen. The exploitability problem at the center of most AppSec programs remains unsolved. Breaches keep happening. Risk scores don't move.