Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI agents have crossed an important line from making suggestions to taking actions. They can read a repository, call internal systems, change code, open a pull request, and keep working while the human moves on.

It happened again. We blinked, and suddenly summer’s over and it’s time to register for classes. The horror!

AI governance reduces security risk by enforcing least-privilege access, protecting the data and credentials AI systems handle and making every AI action auditable. This matters because most organizations deploy AI faster than they can govern it. Employees adopt unsanctioned tools, and autonomous AI agents are created under existing user identities. Each one adds unmonitored machine identities that expand your attack surface – the exact gap that governance closes.

AI is changing how products get made. For user experience teams, that means the very shape of the work is changing.

It's 6:30am and you hear the door of the nightclub you've spent the last 8 hours inside shriek as it closes behind you. You watch bleary-eyed as an overly bright sunrise illuminates the business-suited people as they glide effortlessly along the sidewalk, their obnoxiously well-rested faces talking about work on their fully charged phones. You wonder, "Where did all the fun people go? And what happened to my wallet?".

We studied what happens when Large Language Models (LLMs) generate vulnerability patches for recently disclosed, complex vulnerabilities. Our data shows that LLMs produce Fix-Like Artifacts with Embedded Defects (FLAWED) 53.9% of the time when complex patches are required.

AI has changed the calculus of a credential attack. Before, finding and exploiting credentials in an enterprise environment required time, patience, and human judgment. An attacker had to decide which accounts were worth testing and which systems were worth reaching. Many credentials never made the list.