In this episode, we unpack one of the most overlooked but dangerous risks in AI deployment—insider threats. While organizations often focus on securing data at rest and in transit, there's a blind spot few talk about: data in use.

Imagine a secure, on-prem AI system running in your data center. It sounds safe—but what if a trusted insider with just enough access could dump memory and expose raw, unencrypted sensitive data?

For industries like finance and healthcare, where data privacy is mission-critical, this is a nightmare scenario.

We dive into real-world concerns from companies handling PII, financial transactions, and medical records. They’re skeptical of SaaS AI and even cautious about internal data sharing.

So what’s the fix? It's not just about where AI runs—it's how it's built.

This episode explores why Confidential Computing is critical for truly secure AI. From in-memory encryption to secure enclaves and built-in guardrails, we discuss what a next-gen AI platform must include to defend against insider misuse and keep data secure through every stage of processing.

If you're responsible for AI security or data governance, this episode is your wake-up call.