The rapid adoption of AI often means security is an afterthought. And let's face it—humans are not always great at assessing risk. But how has AI transformed the security landscape? What can the industry do to stay informed and ready to respond to threats? And what does this mean for product security?

Jeff Crume, distinguished engineer at IBM, stops by to talk about AI as "the new attack surface", and explains why the technology, like so many others, can be used for both altruistic and malicious intentions.

Our trust in the internet is the lowest it’s ever been. In spite of our vigilance, we face more threats than ever before. Product security is a vital element in the defense against malicious incursions. This season of Compiler covers the particulars of product security. 

With some help from Emily Fox, Portfolio Security Architect at Red Hat, our hosts kick off the season with a simple question: What is product security?

Phishing. DDoS attacks. Social engineering. These are not new terms if you know anything about cybersecurity. But emerging technologies are making these well-known methods of attack easier than ever.

Bad actors are paying attention—and they are leveling up their skills accordingly.

It isn’t just cybersecurity professionals who have to be aware and responsive– people working in product security are a part of the effort, too. What do they need to know to respond to these newer attacks?

This season, hosts Emily Bock and Vincent Danen will dig into how the security landscape has changed, and how IT professionals can work together to prevent and prepare for whenever, wherever, and however threats emerge.

Sure, AI has made a splash. And it's on us to level up, learn the ropes, and roll with it. But how do we even do that? And what cool human stuff might we accidentally ditch along the way?

The Compiler team ends the season discussing the importance of context, creativity, and applied knowledge.

Agentic AIs are showing promise for tedious work. But it’s hard to explain exactly how you want it done—and getting it wrong could create big problems. 

This episode of Compiler investigates how Agentic AIs could carry out their tasks and how some agents are taking their baby steps in the wide world. The team also considers the difficulties humans have expressing what we want computers to do for us, and how that could create unintended consequences.

There’s no one AI model to rule them all. Each project has its own requirements. Where do you get started building your own model?

Compiler continues its conversations with big dreamers about their big projects—and how they’re piecing together the building blocks of their AI models.