Today, the average enterprise network is like one big game of Telephone: Critical data flows between apps and assets, software systems and their subcomponents, on-prem laptops and cloud storage buckets. Every single gap between the pieces—every single transaction—is a possible vulnerability, a chance to hackers to get in or data to get scrambled. And the introduction of multiple AI models is only making things trickier. Data passes between models, transforms in ways no one fully understands, and emerges on the other side as something you didn't quite expect. In this episode of IBM’s Security Intelligence, Vishal Kamat, VP of Data Security at IBM, walks us through the security challenges of the multi-model AI world: the black box problem, the accountability gap, shadow AI, agent session smuggling, and why less than 1% of enterprise data is actually in models today, even as everyone scrambles to build AI applications. It's Telephone all the way down. But someone has to make sure the message gets through clean. The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Follow the Security Intelligence podcast on your preferred platform: https://www.ibm.com/think/podcasts/security-intelligence

Ask this episode

“What did the council decide on item 42?” — answered with the speaker and the moment.

Transcript

Related