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. Tokenization could help enterprises accelerate AI agent deployments by protecting sensitive information without preventing models from using it, according to Diego Loureda, Protegrity’s vice president of customer success and presales. In a recent interview with theCUBE, 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 therefore account for how information is used and shared across AI workflows. Protegrity’s technology replaces sensitive values with substitutes, rather than relying on conventional encryption that can restrict access to data. Loureda said the method allows protected information to remain available for analytics and AI applications. He also described tokenization as quantum-ready because it uses value replacement in memory instead of a mathematical algorithm. The need for more scalable protection is underscored by research commissioned by Protegrity from Enterpri...