Just How Many Credit Cards Do You Hold?
Many archiving systems on the market—including Symantec Enterprise Vault—now have some great solutions in place to identify and manage data meeting specific sensitive criteria. For example, let’s look at Data Classification Services (DCS). DCS is a great addition to the Enterprise Vault suite and easily categorizes data matching specific patterns, criteria and rules. Sensitive criteria might include social security numbers, credit card numbers or bank account identifiers – exactly the type of data that should not be floating around your messaging systems, regardless of whether or not you have compliance requirements. DCS, however, can only be used by organizations on a forward-looking basis, i.e., it can only apply the classification to new data (it cannot apply it retroactively). While a great product, DCS may also be something that smaller organizations utilizing Enterprise Vault do not want to use or invest the time in configuring and setting up, because there is no compliance requirement.
So what is the solution for historic data? What about smaller organizations that need to identify, and potentially remove, such data? That is where Globanet’s Rule Based Item Manager, also known as RBIM, comes into play. RBIM has the capability—and has been designed from the ground up—to search any indexed Enterprise Vault property, including the body, for specific patterns, using regular expressions. For example, you might want to identify all messages containing a credit card number (####-####-####-####) in the message body, the subject of messages between specific dates or messages between specific senders and recipients, and identify your exposure. You may even want to go a step further and inspect or delete that data. RBIM is designed to do exactly that.
What about the more complex, real-life cases surrounding some of your searches? Credit card numbers are a great example of where, when sharing, the format of the card number might be different than four sets of four numbers. Or, take American Express, which has a different format than other credit cards. Sharing might happen by specifying just the whole 16 digits with no separator, meaning the situation is more complex than just searching for a specific pattern. Given the power of RBIM and regular expressions, however, this isn’t a problem. Searching for sensitive data with loose pattern matching is easy with RBIM. If you’re not sure whether you have sensitive data floating around your data stores, it’s time to find out and take action, if necessary.
RBIM can even help detect sensitive information in other forms of communication, such as instant messages (IM). When combined with Globanet’s Merge1, which archives content of supported IM systems in Enterprise Vault, RBIM quickly and easily extends to identify that data, too. So give us a call, and give RBIM a try!