Common AI Use Cases in Healthcare

The race to deploy AI is on, but are the cloud platforms we rely on secure by default? This episode features a practical, in-the-weeds discussion with Kyler Middleton, Principal Developer, Internal AI Solutions, Veradigm and Sai Gunaranjan, Lead Architect, Veradigm as they compare the security realities of building AI applications on the two largest cloud providers.

The conversation uncovers critical security gaps you need to be aware of. Sai reveals that Azure AI defaults to sending customer data globally for processing to keep costs low, a major compliance risk that must be manually disabled . Kyler breaks down the challenges with AWS Bedrock, including the lack of resource-level security policies and a consolidated logging system that mixes all AI conversations into one place, making incident response incredibly difficult .

This is an essential guide for any cloud security or platform engineer moving into the AI space. Learn about the real-world architectural patterns, the insecure defaults to watch out for, and the new skills required to transition from a Cloud Security Engineer to an AI Security Engineer.

Guest Socials - ⁠⁠⁠Kyler's Linkedin + Sai's Linkedin

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If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:

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If you are interested in AI Cybersecurity, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast

Questions asked:

(00:00) Introduction(02:30) Who are Kyler Middleton & Sai Gunaranjan?(03:40) Common AI Use Cases: Chatbots & Product Integration(05:15) Beyond IAM: The Full Scope of AI Security in the Cloud(07:30) The Role of the Cloud in Deploying Secure AI(13:10) AWS AI Architecture: Bedrock, Knowledge Bases & Vector Databases(15:10) Azure AI Architecture: AI Services, ML Workspaces & Foundry(21:00) The "Delete the Frontend" Problem: The Risk of Agentic AI(23:25) A Security Deep Dive into Microsoft Azure AI Services(29:20) Azure's Insecure Default: Sending Your Data Globally(31:35) A Security Deep Dive into AWS Bedrock(32:30) The Critical Gap: No Resource Policies in AWS Bedrock(33:20) AWS Bedrock's Logging Problem: A Nightmare for Incident Response(36:15) AWS vs. Azure: Which is More Secure for AI Today?(39:20) A Maturity Model for Adopting AI Security in the Cloud(44:15) From Cloud Security to AI Security Engineer: What's the Skill Gap?(48:45) Final Questions: Toddlers, Kickball, Barbecue & Ice Cream