Balancing data security with data utility is one of the biggest challenges for AI and analytics teams. Masking is an important way to safeguard sensitive data, especially for privacy-driven workflows. But when used for training AI/ML models, it changes the format and breaks data relationships, which can leave teams with incomplete data and weaker results.

Our research shows that adding tokenization can preserve data usability, enabling more accurate models, stronger predictions and faster time to value—all while keeping data secure at rest, in motion and in use.

Expect to learn:

• Challenge of balancing data security and utility: Understand the core struggle enterprises face in securing data while making it useful for AI training & workloads.
• Our research findings on tokenization vs. data masking: Gain insights from the research conducted by Capital One Databolt and PwC, which tested tokenization against data masking in real-life use cases, demonstrating significantly higher accuracy scores for AI/ML models using tokenized datasets.
• Capital One Databolt as a premier tokenization solution: Explore how Databolt’s tokenization complements existing encryption and masking, providing a modern, layered data security strategy.