Database Security and Integrity

The security and integrity of data in production databases is also a prime concern that can be addressed with data management tools. Knowing what, exactly, is being done to data and who is doing it is next to impossible without a tool. Implementing auditing and database usage reporting software can help to track down changes made to database privileges, changes to database structures, changes to data, and access to data. Database log analysis tools are useful for security as well because they can be used to examine log entries and format them in a readable fashion to show who did what when to specific pieces of data.

There are also database auditing tools that do not rely on the log but tap into all of the SQL requests and commands going into the DBMS. The SQL is examined and reports can be generated for assuring compliance with industry regulation such as Sarbanes-Oxley, PCI DSS, and others.

Another consideration for database security is the ability to monitor and report on user behaviour. Studies show that internal users are a bigger threat to security than external hackers. Database security tools that can maintain a baseline of standard user behaviour and report on anomalies are quite useful for exposing potential internal threats. For example, if UserX usually logs in and accesses financial data between 9:00 AM and 11:30 AM and then again between 2:30 PM and 4:00 PM every weekday, wouldn’t it be nice to know if this UserX is mysteriously accessing account data at 1:00 in the afternoon on a Sunday?

Of course, database security is a much deeper topic and tools can help to thwart SQL injection attacks and expose and patch potential DBMS vulnerabilities.

Another useful category of data management tool provides data profiling capabilities. Profiling your data is a methodology for gaining insight into your business data and refining your processes for improving data quality. A data profiling tool is used to discover structure, data, and relationship anomalies in your data. Profiling techniques help you to uncover instances of improper data where the data does not match the metadata definition, patterns do not match, values are inaccurate, and occurrences of redundant data. Armed with such information a concerted clean-up effort can be exacted on the data.

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