Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

A firewall rule set to allow any source to any destination can stay live for months. It cancels out the rules beneath it and lets traffic pass unchecked. That kind of drift sets off no alarm. It builds up between quarterly reviews, while small teams govern 50 or more firewalls and hundreds of rule changes a week. Reach Network Security Assurance finds these controls, shows how long each has been open, ties the finding to real exposure, and guides the fix.

AI is accelerating cyberattacks, pushing security teams to their breaking point. Arctic Wolf helps organizations get ahead and stay there with the Aurora Superintelligence Platform, combining AI that is built in, not bolted on, with human validation and 24x7 security operations. See how Arctic Wolf helps protect more than 10,000 customers.

Cybersecurity has reached a turning point. As defenders embrace AI, the question is not simply whether it is powerful, but whether it can be trusted to deliver real value. The Arctic Wolf Aurora Superintelligence Platform brings together trusted AI, real-world data, and human expertise to help transform security operations with reliability, governance, and results.

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.

In this episode, host Richard Bejtlich sits down with Corelight Co-founder and Chief Strategy Officer Greg Bell to unpack groundbreaking research that quantifies exactly how data quality impacts AI-driven security automation. Moving past qualitative industry hype, Greg shares hard evidence from an empirical experiment pitting leading AI agents against real-world Capture the Flag (CTF) challenges and incident response report writing. The findings reveal a dramatic truth: basic firewall and flow logs place a hard cap on inference, throttling an LLM's capacity for deep insight.