A 5-minute AI risk management breakdown
A real-world success case on how a global wealth management institution built an evidence-based AI Risk Management framework for GenAI.
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Introduction
In this exclusive video case study, Angela Carpintieri, Head Analytics & Transformation - CRO at Julius Bär, shares how the institution established a comprehensive AI Risk Management framework to ensure the safe and compliant use of AI across business functions.
Joined by Dr. Petar Tsankov, CEO & Co-Founder of LatticeFlow AI, they explore how Julius Bär translated high-level AI principles into deep, rigorous AI risk controls, turning governance into measurable action.
Fill out the form to access the full video and discover how this collaboration set the foundation for trustworthy and scalable AI.
WHY IT MATTERS
It delivers a blueprint to set up an AI risk management framework to be implemented across the entire organization.
It shows how to connect governance, risk, and compliance frameworks with deep technical assessments to enable transparent, trustworthy AI systems across the organization.
Inside the Case Study
Building AI Risk Governance
How AI principles, risks, governance, management, and controls come together in one integrated framework.
Turning principles into controls
How LatticeFlow AI helped translate ethical AI principles into deep, rigorous AI risk controls.
Scaling trustworthy AI
Lessons learned from establishing an organization-wide framework that ensures alignment and drives AI trust and innovation.
Get Access
Fill out the form to access the case study and discover how this collaboration set the foundation for trustworthy and scalable Al.