Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Japan’s Ministry of Finance reports that AI adoption in large local enterprises has surged to 89%. Companies are progressively choosing to incorporate AI agents into their workflows, building with Microsoft Copilot Studio, ChatGPT Enterprise, Claude, and a myriad of other platforms, all living across a variety of environments with autonomy, privilege, and minimal supervision.

AI Detection and Response (AI-DR), also called AI runtime security, is quickly becoming a must-have. Prompt injection, jailbreaks, and data leakage are real, and the prompt is often where attacks begin. But the prompt is only the first hop. In an agentic enterprise, a single instruction can trigger activity well beyond the LLM, all the way to your business systems and data.

Access to powerful AI models is opening new possibilities for cybersecurity. Models can help uncover weaknesses, investigate threats, and explore how attackers might break into an environment. Using several models can bring different strengths to that work. But model access alone does not answer a critical question: can your defense stop an attack while it is happening? In the frontier AI era, discovery needs to connect to action.

For the last few years, when somebody says “AI,” what they are usually referring to is a Large Language Model, or LLM for short. You chuck some text in, it has a think, and it gives you some more text back. This is brilliant if you want code written, documents summarised, or quantum physics explained to you in a limerick, but while this is great for humans, most software doesn’t actually want a beautifully written paragraph. Software just wants an answer so it can make a decision.

Let’s catch up on the more interesting vulnerability disclosures and cyber security news gathered from articles across the web this week. This is what we have been reading about on our coffee break! A popular shopping site hit as we get closer to a holiday season. Might want to watch out for the usual escalation then.

AI coding assistants are unpredictable by design. Learn how AI hooks add deterministic controls and how GitGuardian ggshield blocks secrets before agents can use them.

Start with what the current backlog already costs: analyst hours spent on triage, developer time lost to duplicate tickets, slow MTTR, and manual reporting. Then tie each benefit of ASPM software to one of those costs, such as fewer tickets per fix or faster remediation. Compare pricing models on your real numbers, including integration upkeep, and propose a measurable pilot on five to ten high-value applications. Most AppSec teams don’t need to be sold on ASPM software.

John White is the Field CISO for EMEA at Torq. A respected security executive with more than 20 years of leadership experience, John previously served as CISO at Virgin Atlantic, where he led a multi-year transformation deploying the Torq AI SOC Platform to modernize cyber operations. Prior to Virgin Atlantic, he built and transformed security functions for global organizations, including ASOS, Liberty Global, AEG Europe, and KPMG.

Every quarter, your board asks the same question, just phrased differently: Are we more secure than we were last time? A point-in-time assessment can't answer it, because it shows where you stood on the day someone ran it, not which way things are moving. Security posture monitoring is the correct response to give to the board. This guide answers the three questions behind every board pack: what goes in front of the board, where the number comes from, and how you show it moved.