Let's Build an AI Privacy Router

Hosted by Katharine Jarmul

Mon, Apr 6, 2026

4:00 PM UTC (30 minutes)

Virtual (Zoom)

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Practical AI Privacy
Katharine Jarmul
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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.

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