Securing the Future: AI Application Security

Machine learning is being adopted faster than ever, and with that comes a growing need to secure not just the data but the models and systems that support it. In this talk, we’ll take a practical look at MLSecOps, a framework for building security into the machine learning lifecycle from the start. This includes protecting sensitive training data, monitoring for adversarial attacks, and securing the infrastructure models run on. Dan will walk us through real world considerations for anyone developing or deploying ML applications and why security cannot be an afterthought.

If you are a data scientist, engineer, or security professional, you will leave with a better understanding of where the risks are and how to reduce them before they become a problem.

👨🏻‍💻 SPEAKER: Daniel Fernandez
Dan Fernandez is a seasoned Product Management Leader with over thirteen years of experience driving innovation in data, analytics, and machine learning products, particularly within the cybersecurity space. Known for his ability to bridge technical and business teams, Dan has led the development of solutions that deliver real impact across a range of industries.

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