You Can’t Patch a Brain: Basic Challenges of AI Security
Hosted by Mahesh Yadav and Dominique Wimmer
Fri, Feb 27, 2026
5:00 PM UTC (45 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course

Fri, Feb 27, 2026
5:00 PM UTC (45 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course

What you'll learn
The Illusion of Guardrails
Why You Can't Patch a Brain
Engineering Robustness with CaMeL - beyond "scaffolding"
Why this topic matters
You'll learn from
Mahesh Yadav
Ex-GenAI Product Lead at MAANG Firms l AI PM Coach l 10k+ Alumni
Mahesh has 20 years of experience in building products at Google, Meta, Microsoft, and AWS AI teams. Mahesh has worked in all layers of the AI stack, from AI chips to LLM and has a deep understanding of how using AI agents companies ship value to customers. His work on AI has been featured at the Nvidia GTC conference, Microsoft Build, and Meta blogs.:
His mentorship has helped various students build real-time products & careers in the Agentic AI PM space.
Whether you're a hobbyist or a professional looking to get a grasp on GenAI Product Management, feel free to join our channels for more such sessions
- Join our Substack
- Join our Linkedin Community of AI PMs
- Follow our YouTube page for all our sessions.
Dominique Wimmer
Product @NYC OTI, Ex-Meta/Google, AI | Safety | Security
Dominique Wimmer has more than a decade of experience building products at Meta and Google, and most recently at the NYC Office of Technology & Innovation. As a Lead Product Manager she builds critical infrastructure for all New Yorkers’ service needs. In her career, she has built AI-powered trust & safety systems at scale, including reducing harmful content through ML-based detection and red-teaming GenAI products. Additionally, currently pursuing a Master's in Cybersecurity at NYU (part-time), Dominique mentors AI product managers on building safe, agentic AI systems for the Agentic AI Institute and serves as a Cohort Leader for Stanford's Ethics, Technology & Public Policy program. Her work bridges the gap between responsible AI principles and real-world implementation at billion-user scale.
Previously at
.png&w=1536&q=75)