Securing AI: Insider Threats and Confidential Computing

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.

Om Podcasten

Decoding AI Risk explores the critical challenges organizations face when integrating AI models, with expert insights from Fortanix. In each episode, we dive into key issues like AI security risks, data privacy, regulatory compliance, and the ethical dilemmas that arise. From mitigating vulnerabilities in large language models to navigating the complexities of AI governance, this podcast equips business leaders with the knowledge to manage AI risks and implement secure, responsible AI strategies. Tune in for actionable advice from industry experts.