White Paper
Discover how tokenization preserves 99.7% of AI model accuracy while protecting personally identifiable information (PII)—allowing your data teams to innovate at scale.
New research from Capital One Software and PwC shows you don’t have to trade data utility for security. See how tokenization helps secure sensitive data to maintain the predictive power of AI/ML models.
While masking and redaction work well for general sanitization, these methods can limit the utility of predictive data. Switching to tokenization in these instances allows teams to maintain data relationships for AI model performance without compromising privacy.
Because tokenized data preserves structure, it can fit into existing data pipelines without major changes to models or data flows. This can be especially helpful during large‑scale platform shifts or cloud migrations.
Data engineers can tokenize data earlier in the pipeline, helping ensure sensitive data is protected from the moment it enters the workflow while maintaining its analytical utility.
