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This blog lists out 6 key metrics to measure the maturity and effectiveness of your Data Loss Prevention (DLP) program. All the metrics are operational and can be measured quantitatively to help you fine-tune your DLP program.
This is the number of exceptions granted over a defined time period. Exceptions are temporary permissions granted on a case-to-case basis. If the Exceptions are not tracked or documented these could result in potential vulnerabilities for exploitation. Ideally, the number of exceptions for a defined time period should remain as minimum as possible
One of the major challenges in DLP program is dealing with false positives. Any mature DLP program within an organisation will try to reduce the false positives to near zero value. This metric is a very good indicator of your Data classification effectiveness, DLP rule-set effectiveness etc.
This is the mean time to respond and initiate action to DLP alerts regarding possible data ex-filtration attempt. This metric is important as most DLP implementations are for alerts only and aren’t put into Blocking mode due to high False-positives. DLP alerts are among the most significant security events that if not prioritised can result in a major data breach. DLP alerts can uncover malicious insider attacks, advance persistent threats
This is the number of unmanaged devices which processes and stores sensitive data. This could be file shares, endpoints, servers etc. Each of these devices is potential egress points for sensitive data. A good DLP program will have all of the devices, that handles sensitive data, managed using DLP tool.
Database fingerprinting is one of the key methods which any modern Data Loss Prevention tool use to protect your sensitive data against possible leakages. Ideally, all the databases holding sensitive data must be fingerprinted and available to the DLP tool. This metric gives an indication of the risks associated with databases which are yet to be fingerprinted.
The first step in any Data Loss Prevention program is data classification. Data classification is done to identify sensitive data wherever it resides. It is imperative to classify databases and other data resident devices so that effective controls can be applied to them. If you are blind about your sensitive data sources your DLP is already a failure. This metric indicates you the number of databases, devices, endpoints, file shares which are still at your blind spots.
Do let me know if you want us to add or modify any of the listed metrics. Check out the Data Loss Prevention market within FireCompass to get more information on these markets.