Tokenization and the Path to Secure Enterprise Innovation
Discover how tokenization preserves 99.7% of AI model accuracy while protecting personally identifiable information (PII)—allowing your data teams to innovate at scale.
Discover how tokenization can eliminate the trade-off between data security and AI performance
New research from Capital One Software and PwC shows you don’t have to trade data utility for security. Learn how tokenization helps secure sensitive data to maintain the predictive power of AI/ML models.
3 benefits of tokenization
1. Maximizing data utility for AI
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.
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.
2. Frictionless integration for secure innovation
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.
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.
3. Protecting the end-to-end data pipeline
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.
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.