Data Protection and Privacy Summit

Traditional perimeter-based security can't protect enterprise AI and cloud environments. As organizations adopt zero trust, policy-driven compliance alone cannot safeguard sensitive assets. These risks carry serious consequences: inadequate data security across global pipelines results in regulatory penalties, data loss and exposure of intellectual property.

Organizations need to move beyond trusting intent and start proving data governance through cryptographic means. This session explores how to operationalize Privacy-enhancing technologies (PET) including fully homomorphic encryption, confidential computing, and differential privacy. These tools allow analytics and cross-border research without exposing raw personal data. We'll look at how privacy and AI governance intersect, and practical strategies for algorithm-based unlearning and data provenance.

Join industry thought leader Yeghisabet Alaverdyan to discover cutting-edge architectures that form resilient, sovereign and verifiable enterprise data ecosystems.

Key Takeaways:
- Adopt verifiable cryptographic guarantees instead of relying on trust.
- Deploy PET in production to enable secure business analytics.
- Mitigate AI-specific risks like inference attacks and unauthorized data exfiltration.
- Implement mechanisms for algorithm-based unlearning and data provenance tracking.
- Secure compliance across complex cross-border data transfer pipelines.