AI accountability is moving from principle to proof. As regulatory expectations mature and AI innovation expands across the enterprise, privacy and governance leaders are increasingly asked not just how their organizations approach responsible AI – but what they can actually demonstrate. That means showing how AI systems and agents are governed, how data and access are controlled, how decisions are documented, and how risks are identified and addressed over time.
In this session, we’ll explore what meaningful AI accountability looks like in practice, and how real world teams can turn evolving expectations into defensible, repeatable processes.
Vous apprendrez :
- What regulators and stakeholders may expect organizations to demonstrate in 2027
- How to translate AI governance principles into evidence of real controls
- Where privacy, security, legal, and AI governance responsibilities intersect
- What documentation and oversight can help demonstrate accountability
- How to build practices that can evolve alongside changing AI systems and requirements
Intervenants :
- Jim Sturm, VP, Legal & Chief Privacy Officer, Inspire Brands
- Dan Hansen, directeur du conseil technique, BigID