Find sensitive user data in databases with sensitive data discovery

Locate data that needs to be masked/anonymized

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BizDataX uses metadata inspection, data sampling, and various discovery rules, algorithms and heuristics to automate the process of locating sensitive data. Sensitive data discovery checks multiple systems, databases, hundreds or thousands of tables, and possibly billions of records.

Automated search for findings

BizDataX and sensitive data discovery start by connecting to one or more databases and inspecting data samples and metadata information. Discovery rules, algorithms, and heuristics are used (e.g. name lists, tax ids, credit card numbers, home addresses) while producing discovery findings. Hit rates and database statistical data can be used to sort and filter the results.

BizDataX Ekobit Automated search for findings
BizDataX Ekobit Classification of findings
Classification of findings

Each finding can be classified as sensitive (hit) or not sensitive (miss). Users can inspect the hit rate, peak the data or check if the same column is classified as finding somewhere else, before making a final decision. Users can add comments and collaborate with the DBA or application development team in case there is a doubt about the data found. Alternatively, one can request a larger sample or modify the rule to get a new and possibly shorter list of findings.

Producing a masking specification

Focusing on finding hits only (i.e. only tables and columns containing sensitive data), the team specifies how to mask the data – which masking algorithms and referential integrity strategies to use. Discovery process results, comments, statistics, hit rates and probabilities are always accessible in context to support the process.

BizDataX Ekobit Specification

Sensitive data discovery in a nutshell

Comprehensive and precise

Automated search inspects the whole system, reducing a chance that some sensitive data is left behind.

Time saving

Manual inspection is focused on a manageable list of candidates only.

Performance with variety of databases

Huge databases can be inspected in reasonable time due to smart sampling and rule evaluations being executed in parallel. Major relational databases are supported: DB2, MSSQL, Oracle and others.

Start discovering sensitive data in your databases