We have pioneered the concept of “ML for the 99%” with the ML at reasonable scale series, and recently discussed what changed with the new AI wave (spoiler: not much, the fundamentals stay!). In the talk we review the basics of ML in production and stress what changed and what didn’t in the era of LLMs.

Generative AI and machine learning systems are reshaping industries but also introducing new security risks. The reliance on vast data, rapid deployment cycles, and automated pipelines in MLOps has expanded the attack surface, exposing vulnerabilities to data poisoning, adversarial inputs, and pipeline exploitation.

This session explores the unique security challenges of ML systems in the GenAI era and provides actionable strategies to safeguard them. Learn why traditional approaches fall short and how to fortify your ML lifecycle to
stay ahead in an evolving threat landscape.

Discover how incorporating continuous human
guidance ensures ethical, accurate, and context-
aware outputs, while autonomous agentic systems
push the boundaries of AI, enabling proactive
decision-making and complex problem-solving.
And see a demo of creating a governed chatbot on
your own company’s data within 60 seconds – not 6
months!