Rethinking Data Discovery with a Privacy Lens

The convergence of exploding data volumes and increasing data privacy regulations are creating unique challenges for businesses. Traditional data discovery methods, such as DLP, are limited to simply classifying data in various categories, such as sensitive, financial, or PII. They were not designed for privacy, and are not effective for providing rights to individuals in an automated fashion aligned with the latest regulations.

Data discovery for privacy requires accurately identifying personal data, which may be broad in scope, and mapping that data back to the individual owner. It also requires data associated with individuals to be automatically discovered in a way that does not create PD sprawl by puting PD in indexes. Delivering this at scale requires automation.

This interactive workshop will feature insights on best practices for data discovery with a privacy compliance lens and is relevant for professionals across data management, information technology, data privacy, compliance, and information security.

Key takeaways gained from this session will include:

Understanding data discovery requirements for privacy

Harnessing bot technology and AI to accurately identify personal data

Optimizing business processes across stakeholders in multiple organizational silos

Leveraging a PrivacyOps framework to maximize ROI