Why can't we just use our existing network DLP to secure AI?

Traditional DLP relies heavily on file types and rigid regular expressions. Generative AI prompts are highly unstructured, contextual text. Standard DLP tools often generate massive amounts of false positives or miss nuanced data leaks entirely when evaluating chat streams.

AI Glossary

Sensitive Data Protection

Controls that reduce accidental disclosure of confidential data in AI workflows.

TL;DR

—Controls that reduce accidental disclosure of confidential data in AI workflows.
—Sensitive Data Protection shapes how organizations design controls, ownership, and operating discipline around AI.
—Use the related terms and explanation below to connect the definition to real enterprise rollout decisions.

In Depth

Sensitive Data Protection (SDP) in the context of generative AI refers to the specific strategies and technologies used to prevent confidential information—such as Personally Identifiable Information (PII), Payment Card Industry (PCI) data, Protected Health Information (PHI), and trade secrets—from being ingested by external LLMs.

The unique challenge of AI is that data leakage often happens unintentionally. Employees routinely paste meeting transcripts, financial spreadsheets, or customer service logs into AI assistants to summarize them, completely unaware that they are transmitting regulated data to a third-party vendor. Traditional Data Loss Prevention (DLP) tools, which were designed to monitor email attachments or USB drives, are often blind to the unstructured, conversational nature of LLM prompts.

Modern Sensitive Data Protection requires specialized, AI-native inline controls. Rather than just blocking the user—which causes frustration and drives them to use unauthorized 'shadow AI' on their personal devices—advanced SDP solutions actively mask the data. For example, if a user prompts 'Summarize this medical record for John Doe, SSN 123-45-678', the SDP engine rewrites the prompt to 'Summarize this medical record for [PERSON_1], SSN [REDACTED]' before it leaves the corporate network, allowing the user to get their summary without causing a HIPAA violation.

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Related Terms

Retention Controls

Configurable settings that define how long AI interaction data is stored and who can access it.

Policy Guardrails

Control checks that evaluate AI inputs and outputs against organization policy.

AI Risk

Potential negative outcomes from AI usage, including policy, privacy, financial, and operational impacts.

Audit Trails

Traceable records of AI activity, governance actions, and control events.

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Glossary FAQs

Traditional <a href='/features/sensitive-data-protection'><a href='/features/sensitive-data-protection'>DLP</a></a> relies heavily on file types and rigid regular expressions. Generative AI prompts are highly unstructured, contextual text. Standard <a href='/features/sensitive-data-protection'>DLP</a> tools often generate massive amounts of false positives or miss nuanced data leaks entirely when evaluating chat streams.

In a sophisticated SDP system, the redaction is bidirectional. When the LLM responds using the masked tokens (e.g., [PERSON_1]), the system re-hydrates the original data (John Doe) before displaying the answer to the user, ensuring a seamless experience.

No. While enterprise agreements usually prevent the vendor from training on your data, the data is still transmitted to their servers, stored in their logs, and processed by their systems. If you are handling highly regulated data (like HIPAA or ITAR), transmitting it at all may be a compliance violation.

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