Keyword Highlighting

The GDPR distinguishes between regular personal data and special categories of personal data (also known as sensitive personal data). Regular personal data consists of, for instance, name, address, telephone number, social security number, and any other data that could directly or indirectly identify a person.

Sensitive personal data, on the other hand, is divided into nine categories, listed below:

The GDPR prohibits the processing of such personal data unless one of the exceptions listed under the GDPR is met:

Keyword Highlighting can be used to highlight certain terms, phrases or patterns in the documents which might be important.

ZyLAB’s Keyword Highlighting feature allows for easy identification of keywords or terms which may be significant to your matter. When a keyword highlighting rule key term is defined and, in turn, highlighted in the document, the reviewer is alerted of the term. Unlike search term hits, which are only highlighted when searched, once a keyword highlighting rule is created, it is applied automatically to the entire dataset in terms associated with the rule that is highlighted in the document viewed.

Keyword Highlighting can therefore be used to, for instance, highlight any sensitive or regular personal data that are present in the documents. If you would search for certain documents belonging to the requester, then these terms would be automatically highlighted in the document, which allows for a better overview. It is also possible - and advised - to use Insights to extract such sensitive or regular personal data.

 

Define a keyword rule that will find and highlight terms in a specific color (not the default yellow).

• On the Home page, select Configuration:
  
  
• Select Keyword Highlighting:
    
• You will see a list of all keyword rules that have already been created. These can be deleted or edited.
  To create a new one, select Create New Item        
  
• Define the Name. 
• Define one or more Keyword Queries.
• Click  

 

Query Syntax Help

For more information—refer to ZyLAB One Search Language Guide.

Use predefined macros, like #amount# or #date#. A "Placeholder #xxx# is not found" message is shown when the macro is not recognized.

• Fuzzy
  
  finds variations of a term.
  Example: dutch~1, will find dutch, ditch, duich, durch, etc.
• Question Mark ?
  

  matches a single character.

  Example: wom?n, will find woman and women.
• Asterisk *
  

  matches zero or more characters.

  Example: *most, will find most and almost.
• AND
  
  finds related terms and narrows your search.
  Example: president AND america, will only find files with both terms.
• OR
  
  finds one or all terms and broadens your search.
  Example, car OR transportation, will find files with only the term car or transportation, and files with both terms.
• NOT
  
  allows you to exclude terms.
  Example: NOT apple, will find all files that do not contain the term apple.
• TO
  

  finds occurrences of a term/query falling between two other terms/queries.

  Example: dear TO sincerely {John}, will find the term John falling between dear and sincerely.
• Within

  finds related terms within a defined range.

  Example: Tom w/2 John, will find the term Tom within two positions (terms) from John (Tom knows John because Tom is his brother).
• Precedes

  finds preceding terms within a defined range.

  Example: live p/2 work, will find ‘live to work’ but not ‘work to live’.
• Number Range
  finds numbers or number ranges.
  Example: (>1 : <10), will find all values between 1 and 10.
• Quorum

  finds a specified number of terms from a list.

  Example: 1 of {blue, green, red}, will find at least one or more colors from the list in a file.
• Character Patterns []

  matches a single character and/or range that is contained within the brackets.

  Example: m[a-z]n, will find all terms with three letters that start with an m and end with an n.
• Negation [^]
  negates a single character and/or range that is contained within the brackets.

  Example: [^b]pple, will find apple, but also –pple, cpple, dpple, cpple, etc. But not bpple.
• Preceding Element +
  matches the preceding element one or more times.
  Example: ab+c, will find abc, abbc, abbbc, abbbbc, etc. But not ac.
• Preceding Element {m,n}
  matches the preceding element at least m and not more than n times.
  Example: ba{2,3}b, will find baab, baaab, but not bab, baaaab.
  
• Preceding Element {m}
  matches the preceding element exactly m times.
  Example: [0-9]{3}-[0-9]{4}, will find local phone number 123-4567.
• Preceding Element {m,}
  matches preceding element at least m times.

  Example: ba{0,}b, will find bb, bab, baab, baaab, etc.

Use Case Example

Enron adds a new Keyword Highlighting rule to highlight terms relating to special category data, such as religious or political beliefs. This can be useful for finding sensitive personal data, which are listed under the GDPR.

It is essential to distinguish between these different sorts of personal data and to ensure that processing is justified (if such personal data are being collected and stored by your company).

The following queries can be used to find sensitive personal data, either by searching or by using keyword highlighting: