Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

‍Artificial intelligence (AI) has departed from the realm of science fiction and emerged as a very real, regular part of life, increasing efficiency across a number of everyday activities. Particularly in the marketplace, where process optimization directly equates to time and money, general-purpose AI (GenAI) and other AI systems have rapidly taken on a central role.

As Anna say in the podcast, “Security reviews show up just when you think the deal is about to close. It’s like a final boss that no one wants to fight.” The last-mile friction caused by security diligence isn’t new, but it’s becoming more painful as deal cycles tighten and expectations around transparency rise. Buyers want answers faster. Vendors want to close faster. And security teams, stuck in the middle, are often left juggling risk, reputation, and revenue timelines.

Over the past few years, artificial intelligence (AI) has transformed millions of organizations worldwide. AI can automate rote tasks, facilitate natural-language interfaces, and pick up subtle patterns in huge data sets. It can also hallucinate wrong answers, reinforce societal biases, and even introduce cybersecurity risks. Before incorporating the technology into their workflows, responsible organizations must weigh the benefits and risks of AI.

It's 8:47 AM. Your phone buzzes with another "urgent" DLP alert. You've already ignored three this morning. This one screams "SENSITIVE DATA DETECTED" in all caps. But it’s just a lunch menu with a credit card number for catering. You silence the notification and grab your coffee. What you don't know? While you're dismissing false alarms, your VP of Finance just dropped next quarter's earnings in a public Teams channel. Your DLP system? Completely silent.