//Abstract

Sahbi, a senior data scientist at SAS, joined us to discuss the various security challenges in MLOps. We went deep into the research he found describing various threats as part of a recent paper he wrote. We also discussed tooling options for this problem that is emerging from companies like Microsoft and Google.

// Bio

Sahbi Chaieb is a Senior Data Scientist at SAS, he has been working on designing, implementing, and deploying Machine Learning solutions in various industries for the past 5 years. Sahbi graduated with an Engineering degree from Supélec, France, and holds an MS in Computer Science specialized in Machine Learning from Georgia Tech.

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Timestamps:

[00:00] Introduction to Sahbi Chaieb

[01:25] Sahbi's background in tech

[02:57] Inspiration of the article

[09:40] Why should you care about keeping our model secure?

[12:53] Model stealing

[14:16] Development practices

[17:24] Other tools in the toolbox covered in the article

[21:29] Stories/occurrences where data was leaked

[24:45] EU Regulations on robustness

[26:49] Dangers of federated learning

[31:50] Tooling status on model security [33:58] AI Red Teams

[36:42] ML Security best practices

[38:26] AI + Cyber Security

[39:26] Synthetic Data

[42:51] Prescription on ML Security in 5-10 years

[46:37] Pain points encountered