Enterprises are turning to tokenization to address data security challenges that are slowing the deployment of AI agents.
Diego Loureda, vice president of customer success and presales at Protegrity, explained that tokenization can protect sensitive information without preventing AI models from using it. He noted 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.
Protegrity’s technology replaces sensitive values with substitutes rather than relying on conventional encryption, which can restrict data access. This approach allows protected information to remain available for analytics and AI applications. Loureda also described tokenization as quantum‑ready because it uses value replacement in memory instead of a mathematical algorithm.
Research commissioned by Protegrity from Enterprise Management Associates found that 83% of surveyed leaders reported project delays, and 82% said they were deploying AI agents with reduced capabilities after removing tools and data to address security concerns.
Loureda recommended shifting from approving individual AI projects to approving reusable governance and security patterns that can support multiple deployments. He argued that tokenization can help security teams maintain control over sensitive data while allowing AI agents to access the information they need, making security an enabler of enterprise AI adoption.
