Privacy Engineering: Safeguarding AI & ML Systems in a Data-Driven Era; With Guest Katharine Jarmul

The MLSecOps Podcast

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Welcome to The MLSecOps Podcast, where we dive deep into the world of machine learning security operations. In this episode, we talk with the renowned Katharine Jarmul. Katharine is a Principal Data Scientist at Thoughtworks, and the author of the popular new book, Practical Data Privacy.

Katharine also writes a blog titled, Probably Private, where she writes about data privacy, data security, and the intersection of data science and machine learning.

We cover a lot of ground in this conversation; from the more general data privacy and security risks associated with ML models, to more specific cases such as the case with OpenAI’s ChatGPT. We also touch on things like how GDPR and other regulatory frameworks put a spotlight on the privacy concerns we all have when it comes to the massive amount of data collected by models. Where does the data come from? How is it collected? Who gives consent? What if somebody wants to have their data removed?
We also get into how organizations and professionals such as business leaders, data scientists, and ML practitioners can address these challenges when it comes to risks surrounding data, privacy, security, and reputation. We also explore the practices and processes that need to be implemented in order to integrate “Privacy by Design” into the machine learning lifecycle.

Katharine is a wealth of knowledge and insight into these data privacy issues. As always, thanks for listening to the podcast, for reading the transcript, and supporting the show in any way you can.

With that, we hope you enjoy our conversation with Katharine Jarmul.

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Additional tools and resources to check out:
Protect AI Guardian: Zero Trust for ML Models

Protect AI’s ML Security-Focused Open Source Tools

LLM Guard Open Source Security Toolkit for LLM Interactions

Huntr - The World's First AI/Machine Learning Bug Bounty Platform