Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

An AI guardrail failure doesn’t come with a warning. One minute, a response goes out. Next minute, it’s a screenshot in the wrong hands, and the question isn’t how it happened. It’s why nobody had defined what the model was allowed to do in the first place. Most teams never asked what the model was actually permitted to do. Deployment happens fast. AI data privacy and leakage prevention aren’t configuration tasks.

Synthetic data for AI development has become the default shortcut for most engineering teams. It’s fast, sidesteps privacy headaches, and lets you move without touching production. I get why teams default to it. But there’s a problem: synthetic data for AI routinely breaks down the moment your system hits real-world enterprise data. The system demos great. It passes every internal test. Then it lands in production and falls apart in ways you didn’t see coming.

Anyone who knows me knows I’m passionate about mindfulness. Because I genuinely believe it makes us better humans. But also, because I have one of those brains that desperately needs it. I’m easily distracted and I start new ideas before finishing old ones. My attention can scatter in a hundred directions. I wrote before how I clicked on a phishing test because I was multitasking and running on autopilot. And that moment really changed the direction of my career and my research.

As I was writing my latest book, How AI and Quantum Impact Cyber Threats and Defenses, I was hit by how many theoretical and real attacks there are involving AI. There are attacks committed by AI and attacks committed agsinst AI, and I’m not sure which category is bigger.

AI security tools are no longer just defensive layers. They are high value targets being studied, fingerprinted, and bypassed much like traditional endpoint detection and response (EDR) platforms and antivirus solutions were in their early days. The speed and scale at which these tools are being deployed makes reactive defense increasingly unsustainable.