Tokenization offers enterprises a way to protect data without limiting AI, analyst says

Enterprises are turning to tokenization to address data security challenges that are slowing the deployment of AI agents. According to Diego Loureda, Protegrity's vice president of customer success and presales, this technology could help accelerate AI agent deployments by protecting sensitive information without preventing models from using it.

Data propagation risks

Loureda warned that once sensitive data enters an AI model, vector store or an agent's memory, it can propagate through connected systems and become difficult to remove. As companies move more agents into production, data protection must account for how information is used and shared across AI workflows.

Research findings

Research commissioned by Protegrity from Enterprise Management Associates underscores the need for more scalable protection. According to Loureda, 83% of leaders surveyed reported delays in their projects, while 82% said they were deploying AI agents with reduced capabilities after removing tools and data to address security concerns.

Governance shift

Loureda argued that companies should shift from approving individual AI projects to approving reusable governance and security patterns that can support multiple deployments. He described tokenization as quantum-ready because it uses value replacement in memory instead of a mathematical algorithm.