Let's Build an AI Privacy Router
Hosted by Katharine Jarmul
In this video
What you'll learn
Evaluate Privacy Guardrails
Investigate via hands-on exercises how external algorithmic guardrails work for privacy routing.
Minimize your Inputs
Build in privacy by investigating how much information you need and tuning your workflow.
Who, What, Where
Who do you trust with what data? We'll explore how to make on-the-fly decisions that follow your choices.
Why this topic matters
As LLMs go agentic and coding assistants develop the skills to read all files on your computer, it's imperative to start thinking through just how you're going to manage sensitive data flows in generative AI systems.
This lightning lesson explores that edge, looking at what might become a necessary part of your AI infrastructure: privacy-based routing decisions!
You'll learn from
Katharine Jarmul
Privacy and Security in AI/ML Systems
Katharine Jarmul focuses her work and research on privacy and security in data science, deep learning and AI. She is author of the well received O'Reilly book Practical Data Privacy (O'Reilly 2023) and has more than 10 years experience in machine learning/AI where she has helped build large scale AI systems with privacy and security built in. You can follow her work via her newsletter, Probably Private or on her website.
Go deeper with a course
Practical AI Privacy
Katharine Jarmul
Author of Practical Data Privacy, Specialist in AI/ML Systems
Keep exploring
