identifying

To detect PII and re-identification risks, this Skill analyzes source code to identify direct identifiers and quasi-identifier combinations, mapping threats to LINDDUN category I. It traces data flows across exports, APIs, logs, and databases to flag exposure.

Quasi-identifiers are data attributes like zip codes, birth dates, and gender that, when combined, can uniquely identify individuals. This Skill detects these combinations in your code to prevent re-identification attacks on supposedly anonymous data.

You can audit anonymization logic by analyzing data exports and API responses for insufficient data masking. This Skill verifies the effectiveness of your masking and flags code paths that inadvertently reveal individual identities.

Yes, you can scan your codebase for LINDDUN identifiability threats. The Skill supports various analysis depths, ranging from quick grep pattern matching to deep data flow tracing and re-identification risk modeling.

Yes, it analyzes data exports, APIs, and logs for privacy compliance by detecting direct PII exposure and combined quasi-identifiers. This ensures user data shared externally for analytics does not inadvertently reveal individual identities.

The best way to check code changes for identifiability issues is to run a targeted analysis on the current modifications. This quickly flags any newly introduced direct identifiers, quasi-identifier combinations, or insufficient anonymization logic.