Securing organizations ML & LLMops deployments: A platform architect's journey onboarding LLM & MLops tools and securing multi-cloud data access
Kyler Middleton, Sai Gunaranjan
fwd:cloudsec North America 2025 · Day 2 · Track 2 - Crestone
Kyler Middleton (Principal Developer for Internal AI Solutions) and Sai Gunaranjan (Lead Architect, Cloud Platform Team) from **Veradyne**, a U.S. healthcare company, delivered a dual-track presentation covering the practical realities of securing AI deployments across AWS and Azure. Rather than presenting novel research or vulnerability findings, the talk functions as an architectural reference guide from practitioners who are building and securing production AI systems in a regulated industry. They cover AWS Bedrock's architecture, capabilities, and security gaps; Azure AI services' deployment patterns, network controls, data access patterns, and policy enforcement; and the broader platform engineering principles that underpin secure AI adoption. The talk's value lies in its candid documentation of what works, what is broken, and what is missing from both cloud providers.
AI review
A competent architectural walkthrough of securing Bedrock and Azure AI deployments, but this is a platform engineering talk, not a security research talk. No vulnerabilities, no exploits, no novel attack techniques. The Bedrock security gaps are useful to know about, but cataloguing missing features is not the same as breaking things. This belongs at a DevOps or cloud architecture conference, not in a security research track.