Without technology, the work of identifying information has to be done manually. This process can be time-consuming or even unfeasible. With the DLP tool, it is possible to adopt techniques that will help to mine all these elements. See some of them below.
Rule-based regular expressions
The system uses an analysis technique based on specific rules . For example, numbers with 16 digits generally correspond to credit cards, while those with 11 digits refer to CPF numbers. Basically, the method adopts patterns that facilitate the identification of this information.
Fingerprint in database
With this methodology, the engine analyzes ecuador telegram phone numbers exact matches for pre-provided structured data. In other words, it identifies what is present in the database and considers the rest as what is in use or in motion.
Exact file match
With this DLP strategy, the system searches for documents based on a hash function – an algorithm that maps large and variable-sized data and “summarizes” it to a fixed dimension. Thus, the strategy is to group the information according to the exact file match, rather than the content itself.
Partial document matching
In this mode, a search is made for data that matches the patterns – size, for example – previously defined. In this case, partial matching does not apply any changes to the data separation, which happens in the previous example.
Statistical analysis
Some DLP solutions feature machine learning. For example, Bayesian analysis—a methodology that involves evaluating hypotheses using maximum likelihood—triggers policy violations on safe content .
To do this, a large volume of information needs to be digitized. The DLP tool scans the data based on rules and separates it, creating ways to protect it through encryption.