Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Agentic AI Security Buyer's Checklist: 15 Questions to Ask Before You Sign

Buying agentic AI security software is a fast-moving decision with high stakes. Get it wrong, and your security team ends up chasing agent activity it cannot see, while attackers exploit business logic gaps that no prompt filter was built to catch. This checklist gives security and platform leaders a structured way to evaluate vendors before signing, based on the questions that actually separate a purpose-built platform from a bolted-on feature.

Seeing Every MCP Connection: Zenity Joins the Cursor Marketplace

Cursor has become one of the primary AI coding environments for development teams, and its agents increasingly reach into the outside world through MCP servers: databases, ticketing systems, cloud consoles, and internal APIs. Every connection extends what an agent can do. It also extends what could go wrong if that access goes unmonitored or unchecked.

Preparing for an AI-Powered Future with Amazon CSO Steve Schmidt

Steve Schmidt, SVP and CSO at Amazon, sees firsthand how both defenders and adversaries are using AI to their advantage. In this episode, he joins Adam and Cristian to discuss modern AI models, evolving adversary behavior, and how Amazon is responding to shifts in the threat landscape. “The big change we’ve seen recently is the ability of models to chain things together to produce something interesting,” Steve says. As AI models grow more adept at automatically chaining and acting, he adds, the time to respond has dramatically decreased.

What an AI Correlation Rule Does When Sources Disagree

A correlation rule joins records from several sources to establish that one thing happened. Two of those sources return different answers about the same identity, the same session or the same action. Something has to happen next, and what most systems do is pick a winner. ‍ Picking is the wrong default. The disagreement carries information that resolving it discards, and in a few specific cases the disagreement is the most useful thing the system produced. ‍

Securing AI API Keys From Development to Production

An AI feature can reach production before anyone has decided who owns its credentials. A developer creates an API key for a prototype, a colleague copies it into a background worker, and a troubleshooting session puts the same value into a support ticket. The application works, but the team can no longer say exactly where its access begins or ends.

Seedance 2.5: AI Video Is Learning to Handle What Happens Between the Shots

Turning a photograph into moving footage has become familiar. The details that make people keep watching usually come from the relationships inside the frame: why a character turns at that moment, why the product appears there, why the music lands on that beat, and how the camera carries the viewer toward what comes next.

OWASP Top 10 for Large Language Model Applications: Complete Guide to LLM Security Risks

Companies rush to utilise the potential of large language models; however, every new use case of generative AI introduces attack vectors previously unknown in traditional web security. The present guide provides an overview of the official OWASP GenAI LLM Top 10 2026 list and explains the appearance of each vulnerability in practice along with mitigation recommendations.

A Complete Audit Trail That Names No One

An AI assistant reads four hundred documents across a tenant. Every read is logged. The application is named, the file is named, the timestamp is exact, and the access is attributed to an account that belongs to nobody. ‍ The audit trail is complete and it cannot answer the question an auditor asks. Nobody asks whether an access was recorded. They ask who reached the data and whether that person was authorized, and a shared service account answers neither. ‍