p>Two basic types of data discovery grammars (rule sets) are used to describe the entities you are trying to identify: curated and user-generated.
The grammars include:
PII: Personally identifiable information, which can differ from region to region (including format, which can cause false positives)
PHI: Personal health information, typically associated with the North American health industry
PCI: Personal credit card information
PSI: Personal security information, for account details access keys
Look for curated and optimized grammars, which can’t be modified by the user. These grammars use context and landmarks for more accurate results and provide a “confidence score” to help you filter out false positives. Context and landmarks can be phrases, single words, or individual characters.
Context is key. File analysis solutions that use proximity to the entity candidate and the strength of the context (based on natural-language processing techniques) contribute to confidence scores. You can obtain more granular scores by leveraging comprehensive lists of specific entities, countries, or regions.
Tuning and flexibility. If none of these grammars covers your specific use case, you can use a file analysis solution that allows for creating custom grammars. These grammars are typically defined by using format-descriptive RegX or simple lists.