Introduction to Regular Expressions in Intella

Introduction to Regular Expressions in Intella

When performing complex searches in Intella, standard keyword search may not always be sufficient to find exactly what you're looking for. This is where Regular Expressions (RegEx) come into play. Regular expressions are powerful tools that enable you to search for patterns or specific formats within your data, such as credit card numbers, email addresses, symbols, or any other structured information. RegEx provides the precision needed for more detailed and targeted searches, making it an essential feature for forensic investigators and eDiscovery professionals working with large data sets.



Using Regular Expressions in Intella

Regular Expressions (RegEx) in Intella are powerful tools for enhancing search capabilities by allowing users to define specific patterns in text. Here's a quick guide on how to use them:

  1. Accessing RegEx Search:

  • Navigate to the Facet Search inside of Intella


  • Click on “Content Analysis” facet to reveal the default categories

 

  • Select the "new" button to bring up the “New Content Analysis Category”



  • Name your category and enter in your regex syntax then click OK



  1. Creating a RegEx Pattern:

  • Running a Search:

    • Next choose your file type filter and click the search button.



    • Click on the cluster to to reveal your files in the details pane

    • Select the files you want to analyze, right click. choose process, and select content analysis


    • For demonstration purposes we are only interested in the running the Emails expression across these items, then select run





    • Reviewing and Refining:

    • After running a search, review the results in the “Content Analysis” facet. Ensure the RegEx is capturing the intended data. Adjust your pattern as needed for more accurate results.








    By mastering RegEx in Intella, you can significantly streamline your data search and analysis, making it easier to pinpoint specific information within large datasets.