Data Protection and Privacy Summit

Organizations regularly vet vendors, complete successful pilots and satisfy baseline compliance requirements assuming that their anonymized data will remain secure. But transitioning clean synthetic datasets into the real-world production introduces significant risks.

Privacy-enhancing technologies that perform well in testing environments often collapse under scale. Static anonymization mechanisms fail when faced with new attack vectors, cross-data stitching and daily operational drift. This risks exposing enterprises to regulatory breaches and severely damaging customer trust.

Tune into this session as Oksana Denesiuk presents a practical framework for governing privacy-enhancing technologies beyond the pilot stage. Learn how to align technical safeguards with legal requirements so your privacy architecture holds up under real-world production demands.

Key Takeaways:
- Understand why synthetic and de-identified data often fail re-identification tests in production.
- Integrate technical privacy controls with legal and regulatory compliance requirements.
- Build threat models and validation protocols that address production-scale risks before deployment.
- Assign clear internal ownership for monitoring, vendor oversight, and audit-ready documentation.
- Implement lifecycle governance that scales with your data ecosystem and supports continuous risk management.