What AI Operator-First SOC Looks Like, and Why It Matters Now

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.

Understanding shadow AI in your endpoint environment

Generative AI–and large language models in particular–reached mass consumer adoption beginning in late 2022 and early 2023, with ChatGPT reaching 100 million users faster than any consumer application in history. Since then, AI has advanced at a breakneck pace and now seems to be incorporated in every tool, app, and website–regardless of how useful it might actually be.

A Look At GitGuardian's ML-Powered Contextual EnrichmentAnd Incident Scoring

In this quick introductory video, Mathieu Bellon, Senior Product Manager at GitGuardian, sits down with Dwayne McDaniel, Developer Advocate, to cover some of the advancements GitGuardian has made by integrating machine learning directly into the secrets security platform. Mathieu describes how engineers and responders can save serious time as by automating contextual analysis, geving the humans in the loop with the best information to be able to take an informed action when it comes to secrets leaks. They also discuss the security implications and where teams can look if they want to opt out or bring their own agents.

Announcing Justification Coach: AI-Powered Guidance for Better Access Requests and Stronger Audits

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.

7 Generative AI Security Risks and How to Defend Your Organization

Generative AI creates new attack surfaces that traditional security tools were not designed to address. The biggest generative AI security risks include prompt injection, data leakage, shadow AI, compliance exposure, model poisoning, insecure RAG pipelines, and broken access control. Each one requires a specific defense, not a generic firewall or DLP rule.

"It's Quite a Shock": The Quantum Deadline Is Real

In this World Quantum Day special edition of This Week in NET, host João Tomé is joined by Bas Westerbaan (Principal Research Engineer) and Sharon Goldberg (Senior Director, Product) to explain why the timeline for post-quantum cryptography may be arriving sooner than expected. Recent research suggests the number of qubits required to break today’s encryption could fall dramatically, accelerating the urgency for companies and the Internet ecosystem to migrate to post-quantum security. Google has set a 2029 migration target, and Cloudflare is working toward a similar timeline.

Axios CVE-2026-40175: a critical bug that's... not exploitable

It’s been a chaotic few weeks for Axios. First, a major supply chain attack put the package under scrutiny. Then, just days later, headlines started appearing about a “critical 10/10 vulnerability” that could lead to full cloud compromise. If you’ve read the coverage, you’ve probably seen claims like: That sounds bad. But when you look closely at how this vulnerability actually behaves in real environments, the story changes.

New KnowBe4 Agent Risk Manager Addresses Pervasive AI Agent Risk

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.