Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Talk to Your Platform: Spin Up JFrog Self-Service Trials with MCP - No Human Intervention Required

JFrog is one of the first Software Supply Chain Management and Security Platforms to provide MCP functionality, which we have now opened up to anyone interested in trying Claude and Cursor in their own development environment. Doing a free trial is one of the best ways to see how JFrog integrates with your developers, operations and security.

Five Signals, One Answer: Why Single-Signal AI Security Always Fails

The security industry hasn’t been wrong about agentic AI risk. It’s been incomplete. There’s no shortage of single-signal solutions for the problem: tools that analyze prompts for malicious content, platforms that monitor data access patterns, capabilities that assess model behavior for signs of manipulation. Each captures something real. None is sufficient on its own.

AI in Australian schools: Managing emerging risks while building a safer learning environment

AI is everywhere in Australian schools. Students are using AI-powered tools to support learning, teachers are leveraging AI to improve productivity and lesson planning, and school administrators are exploring new ways to simplify operations. But while AI holds a lot of promise, it also introduces new cybersecurity challenges. Australian schools increasingly find themselves balancing innovation with the need to protect students and staff.

The New Security Risks of the Agentic Development Lifecycle

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.

Third Party Risk in the Age of AI. A Spotlight on Black Kite

Your vendors are adopting AI faster than you can assess them. What does that mean for your third party risk? Welcome to Razorwire, the podcast where we share our take on the world of cybersecurity with direct, practical advice for professionals and business owners alike. I'm Jim and in this Spotlight on Technology episode, I'm joined by Jeffrey Wheatman, Senior Vice President and Cyber Risk Strategist at Black Kite. Jeffrey previously spent over a decade as an analyst VP at Gartner, where he launched their third party cyber risk management coverage.

Exposure Management in the AI Era | Introducing EDR Compensating Controls Awareness

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.

Where Should Humans Sit in AI-Driven Cybersecurity?

There is a huge amount of excitement right now about AI and security operations. Across the industry, we are seeing rapid innovation in areas such as behavioural analytics, AI-assisted investigation, and increasingly agent-based capabilities designed to help security teams process large volumes of activity more effectively. Security teams need that help. The scale of alerts, identities, and telemetry they must manage today has grown far beyond what humans alone can realistically handle.

AI vs. AI: Fighting the Next Wave of Cyber Attacks with Ravid Circus

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.

Gen AI Pentesting: A Technical Guide for Security Teams

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.