Enterprise AI Unblocked

Agents need real data to deliver value, and that data is full of sensitive information. The more agents an enterprise deploys, the larger the risk surface becomes, as every prompt, MCP connection, and memory store opens a new path for PII to reach a model. To scale AI safely, data needs to be sanitized before it enters the model.

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This whitepaper explores how Runtime AI Data Control sanitizes sensitive data before it reaches any model, without losing the context agents need. It covers how sensitive values in text, documents, audio, and images are replaced with context-preserving tokens that models can still reason over, and how real values are restored only for authorized users and agents.

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