Machine Learning Street Talk (MLST)

#48 Machine Learning Security - Andy Smith

Mar 16, 2021

Andy Smith, a cybersecurity expert and YouTube content creator, dives into the often-overlooked realm of security in ML DevOps. He highlights the importance of threat modeling and the complexities posed by adversarial examples. The conversation sheds light on trust boundaries in machine learning systems and the need for a collaborative approach between ML and security teams. Andy also discusses the unpredictability of state space and the essential role of human oversight, advocating for a pragmatic focus on risk management to enhance data integrity.

37:27

Creator website

Episode guests

Andy Smith

AI Chapters

Episode notes

Intro

2min

Navigating Machine Learning Security in DevOps

13min

Navigating ML DevOps Challenges

5min

Navigating Security in Machine Learning

7min

Securing Machine Learning Systems

6min

Navigating Centralized Security Challenges and Best Practices

4min

First episode in a series we are doing on ML DevOps. Starting with the thing which nobody seems to be talking about enough, security! We chat with cyber security expert Andy Smith about threat modelling and trust boundaries for an ML DevOps system.

Intro [00:00:00]

ML DevOps - a security perspective [00:00:50]

Threat Modelling [00:03:03]

Adversarial examples? [00:11:27]

Nobody understands the whole stack [00:13:53]

On the size of the state space, the element of unpredictability [00:18:32]

Threat modelling in more detail [00:21:17]

Trust boundaries for an ML DevOps system [00:25:45]

Andy has a YouTube channel on cyber security! Check it out @

https://www.youtube.com/channel/UCywP24ly6h6NTusX88TQKTQ

https://www.linkedin.com/in/andysmith-uk/

Video version:

https://youtu.be/7Tz-3S4lypI

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