Description
AI systems can fail in ways that classical software risk methods do not fully capture. Models learn from data, behave probabilistically, and interact with humans and organizations in complex, real-world contexts. Risks can emerge from data, model behavior, user interaction, security exposure, operational drift, or unclear ownership, not just from “bad accuracy.”
In this one-day (online) workshop, AIQ shows how to identify, structure, and manage AI risks across the full lifecycle: from idea and data sourcing to deployment, monitoring, and incident response. Through guided hands-on exercises, participants learn to build practical artifacts (risk file, control plan, governance + ops hooks) tailored to real-world projects. Participants learn how to decide which risks require controls, why, who owns them, what evidence is required, and what to do when failures occur.
This training is designed for professionals involved in building, integrating, or overseeing AI systems. It serves software developers, ML/AI engineers, and data scientists who work directly with AI technologies, as well as QA and test engineers or technical leads who require not only metrics but also clear risk structures and ownership models. It also supports product managers, security specialists, and experts in data governance and compliance - those responsible for translating identified risks into informed organizational decisions.
Categories
Distribution: virtual
Talk language: English
Ticket cost: Paid access