PII Masking

Security
About 1 min read

A security technology that replaces Personally Identifiable Information (PII) such as names, phone numbers, and resident registration numbers within data with specific symbols or dummy data to prevent privacy exposure during AI model processing.

Also known as
Data AnonymizationRedaction

Detailed explanation

A crucial procedure for detecting and processing sensitive information contained within AI training data or real-time prompts. As of 2026, 'context-based masking,' where LLMs understand context to obscure identifiers within unstructured text, has become mainstream, moving beyond traditional regular expression-based methods. In particular, in accordance with the Personal Information Protection Commission's 'Risk-based Guidelines (March 2026)' in South Korea, a flexible masking strategy tailored to the AI service environment (internal vs. external) and data risk levels has become key to securing legal compliance and maintaining data utility for enterprises.

Why It Matters in AI Tool Selection

Since LLMs can train on input data or record it in operational logs, transmitting corporate data without masking leads directly to security incidents. As demonstrating a company's 'risk-based management efforts' in the event of a data breach has become a core element for reducing legal liability starting in 2026, choosing systematic masking tools that go beyond simple filtering is essential.

What to Check When Selecting a Tool

  • Does it provide high detection accuracy for Korean-specific PII (resident registration numbers, Korean addresses, names, etc.)?
  • Can it securely restore (unmask) the original information even if the LLM changes the word order or phrasing of the response?
  • Does the latency during real-time prompt processing avoid affecting the quality of service?
  • Does it support the configuration of risk-based masking policies according to the latest 2026 guidelines?

Application Example

It complies with security policies by converting customer service text like 'Check outstanding balance for customer Hong Gil-dong (010-1234-5678)' in real-time into 'Check outstanding balance for customer [PERSON_1]([PHONE_NUMBER_1])' before transmitting it to the LLM.