Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Calling a model too dangerous to release ignores the obvious reality that open and alternative models will soon reach similar capability. Once the path is visible, other providers, including overseas competitors, will build their own versions, so secrecy becomes a temporary market move, not a lasting safety strategy.

The volume of AI security research is now too high for any human to track properly by hand. The practical answer is using AI to filter AI, reducing hundreds of articles and reports into a daily shortlist so analysts spend their time on signal instead of noise.

The old belief that open source means every bug gets spotted quickly falls apart when nobody is truly looking and nobody works for free. If a flaw offers no bounty, no commercial reward and little public attention, it may sit quietly for years while everyone assumes someone else checked it.

The real shock is not a restricted model with scary branding, it is what standard public AI tools already make possible. A prolonged attack against government systems, tax records and voter data shows the threat comes from scale and persistence, not only from the newest frontier release.

This episode looks at what happens when AI starts finding vulnerabilities at scale, restricted access creates market imbalance, and security teams struggle to keep pace. It covers fragile infrastructure, bug brokers, overloaded analysts, CISO fear, and the growing sense that cyber defence is entering a faster and harsher era.

The fear is not only what advanced AI can do, it is what it can do to brittle systems already running on neglect and compromise. When critical infrastructure is patched together with ageing controls and restricted tools land in a few powerful hands, the imbalance gets worse fast.

The AI bubble is about to burst. Energy costs, chip shortages and computer pricing are reaching unsustainable levels. The economics don't add up anymore and something has to crack. In this episode of Razorwire Raw, Jim Rees explains why AI is hitting an economic wall nobody's talking about. World energy consumption is climbing vertically because of AI. Data centres are on hold because there isn't enough electricity. GPU, RAM and CPU prices are spiralling. Large language model providers are raising prices because compute costs are exploding.

What happens when an AI model can find more vulnerabilities in a day than a red team could find in a year? 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 episode, I’m joined by Martin Voelk, penetration tester and AI red teamer, and Jonathan Care, lead analyst at KuppingerCole covering AI and cybersecurity.

Security teams are drowning in new models, new features and new failures faster than humans can meaningfully respond. Human in the loop sounds sensible, until the volume of AI generated findings and constant change leaves even experienced practitioners unsure where this is all heading.

This episode looks at what happens when AI starts finding vulnerabilities at scale, restricted access creates market imbalance, and security teams struggle to keep pace. It covers fragile infrastructure, bug brokers, overloaded analysts, CISO fear, and the growing sense that cyber defence is entering a faster and harsher era.