Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

For decades, security leaders have optimized defenses in two dimensions. Doors, locks, fences, cameras, access badges, identity systems, and multi-factor authentication have all been designed to control who and what moves through physical and digital perimeters. But as experts discussed during RSAC 2026, something fundamental has changed: the threat landscape has gone airborne.

Anthropic just released Claude Mythos Preview. They did not make it publicly available. That decision alone should tell you everything you need to know about what this model can do. During internal testing, Mythos autonomously discovered and exploited zero-day vulnerabilities across every major operating system and web browser. It found a 27-year-old bug in OpenBSD. A 16-year-old vulnerability in a widely used media codec.

In the fifth installment of SafeBreach’s AI-First series, VP of Development Yossi Attas explores how the development team’s AI-First philosophy is being extended to the customer frontier and improved upon through the Anti-Hallucination Protocol.

Python AWS Lambda functions are ephemeral and highly distributed, which creates security visibility gaps that traditional perimeter defenses and proxy-based controls struggle to fill. Techniques such as credential stuffing, SQL injection, and server-side request forgery (SSRF) can look like legitimate application traffic, making them difficult to identify without visibility inside the application itself.

By Roger A. Grimes and Matthew Duren AI agents can deliver incredible productivity gains, but their operational complexity makes effective threat modeling harder than ever, including for developers, administrators and especially end users. At the same time, both developers and non-developers are increasingly vibe-coding, or using AI to generate functional software from natural language prompts.

Today, we’re introducing Justification Coach, a new AI-powered capability that helps users write better access request justifications in real time, so admins get the context they need for audits and investigations without having to chase people down after the fact.

There is a version of AI SOC that most security teams are familiar with. It summarizes alerts. It surfaces recommendations. It tells an analyst what to look at next. It is useful in the way a well-organized report is useful: it saves time reading, but the work still happens at a human pace. That version of AI is not what this blog is about. For MSSPs and SecOps teams operating at scale, advisory AI is not a destination. In fact, it presents a bottleneck in a different form.

Picture a SOC that investigates its own alerts, hunts threats across customer tenants, isolates compromised endpoints, and writes its own detection rules. Envision the same SOC attacking itself every morning to find the gaps it missed, all before your analysts arrive for the day. This is not a roadmap item, but an operational reality on LimaCharlie. It’s what agentic AI security looks like on a platform built to support it.

The agentic AI security standards your enterprise will adopt in the next 18 months are being written right now, inside working groups most CISOs have never heard of. The Coalition for Secure AI (CoSAI), an OASIS Open Project with more than 45 sponsor organizations, including Google, Microsoft, NVIDIA, IBM, and Meta, is producing the frameworks, reference architectures, and secure design patterns that will define how autonomous agents operate inside enterprise environments.

A few weeks ago the world was exposed to Mythos, Anthropic's new frontier model and the Project Glasswing announcement that came with it. The reaction across the industry was immediate. Cybersecurity stocks fell sharply. The Treasury Secretary convened an emergency meeting with major bank CEOs. 250 CISOs produced a response playbook over a single weekend. That is not a typical announcement or a PR "leak". That is a reckoning. Then, about a week later, I came across MOAK.