Summary: Privacy programs often become either checkbox exercises or roadblocks to analytics and innovation. This session focuses on building privacy programs that protect the organization while enabling AI and data-driven decision-making at speed.

Outcome: A modern operating model for privacy that enables growth

Takeaway: A privacy-by-design playbook for analytics and AI initiatives

Key Discussion Points:

• Privacy that accelerates: enabling trusted data use without constant escalations
• Data classification + inventory: focus on high-risk flows, not 'boil the ocean'
• Purpose limitation + minimization: collecting and using only what you need
• Retention and deletion discipline: reduce exposure and cost
• AI + privacy: what data is acceptable for AI use cases (and what isn't)
• DPIAs/PIAs that work: lightweight assessments with real value
• Vendor data processing controls: subprocessors, cross-border, and access boundaries
• Privacy incidents: how to contain, notify, and recover without business disruption
• Metrics that matter: approval cycle time, rework reduction, incident trend reduction
• Governance integration: privacy + security + compliance as a single decision system