Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The AI singularity may already be here, or far closer than most businesses and governments are prepared to admit. While regulators move slowly, AI capabilities are scaling so fast that power, security and control may already be shifting beneath our feet.

AI coding is disrupting the SaaS market. GRC tools, CRM products and enterprise dashboards that cost hundreds of thousands can now be built internally in weeks using Codex and other AI models. SaaS companies know this is happening, which is why they're locking customers into three to five year contracts. But there's a serious security risk nobody is talking about. If you're vibe coding internal tools and applications, you need continuous security testing to catch vulnerabilities before attackers do. The faster you develop, the faster you need to detect breaches.

AI is breaking modern encryption faster than expected by accelerating attacks on the mathematical problems that protect digital trust. That puts cryptography, secure communications and long term cyber resilience under real pressure as post quantum cryptography becomes more urgent.

AI companies are refusing to release models because the rules around safety, surveillance and misuse are still too weak. When governments cannot keep pace, private companies end up deciding how powerful AI models should be used, controlled and withheld.

The EU just pushed back its AI Act enforcement by 16 months. The US is deregulating. China is governing through infrastructure. And the UK is doing nothing and hoping for the best. 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 Richard Cassidy, Field CISO at Rubrik, and Jonathan Care, Head of the AI Practice at KuppingerCole.

The AI governance crisis is already here, with innovation moving faster than regulation, oversight and accountability. The real problem is no longer only what AI can do, but who governs it when these systems start reshaping security, business and public life.

AI regulation is failing faster than AI itself, leaving accountability, security and control dangerously behind the pace of adoption. This episode explores AI governance, encryption, singularity, super AI and why the future may belong to whoever controls the most powerful models first.

Defence in depth changed forever with cloud security because the old perimeter gave way to platforms, suppliers and connected services outside direct control. That means modern attackers often reach the target through weaker vendors, cloud services or supply chain links instead of attacking the business head on.

Alert fatigue is becoming an AI attack strategy because attackers now have the scale to flood defenders with noise and bury the real intrusion. Instead of one distraction, offensive AI supports constant false signals that overload analysts, stretch the SOC and reduce the chance of seeing what matters.

A prompt injection attack through server logs becomes possible when hidden instructions are processed later by an AI log analysis tool. That turns normal logging into a possible data exfiltration path and gives attackers a new way to abuse LLM workflows without touching the main application logic.