Data Loss Prevention in a Nutshell

Enterprise Security Magazine | Wednesday, August 17, 2022

Data security may be categorized and given priority using DLP solutions. These solutions may also help guarantee that access policies adhere to legal requirements such as HIPAA, GDPR, and PCI-DSS.

Fremont, CA: By prohibiting end users from transferring crucial data outside the network, data loss prevention (DLP) aims to increase information security and safeguard company data against data breaches. DLP also refers to the tools that allow a network administrator to keep track of the information end users access and share.

Data security can be categorized and given priority using DLP solutions. These solutions may also get used to guarantee that access policies adhere to legal requirements such as HIPAA, GDPR, and PCI-DSS. DLP tools may do more than detect; they can notify users, impose encryption, and isolate data.

How does DLP function?

There are two primary technical DLP methods:

• Context analysis examines the document's information or other attributes, such as the header, size, and format.

• Reading and reviewing a document's content to see if it contains sensitive information is known as content awareness.

Both of these strategies are combined in modern DLP systems. DLP initially determines if a document can get categorized by looking at its context. If the context is insufficient, it uses content awareness to search within the document.

• Rule-based - examining a document's content using predetermined rules or regular expressions, such as looking for social security or credit card numbers. Due to its simplicity in processing and configuration, this method is particularly successful as an initial filter, although it typically gets used in conjunction with other strategies.

• Dictionaries - The DLP system can locate terms that denote sensitive information in unstructured data by integrating the usage of dictionaries, taxonomies, and lexical rules. This necessitates detailed data customization for each company.

• Exact data matching - this technique establishes a "fingerprint" of the data and looks for exact matches in a database dump or an active database. One disadvantage of this approach is that making a data dump or using live databases might negatively impact performance.

• Exact file matching searches for files that match a hashed representation of the complete file. Although quite precise, this method cannot be applied to files with numerous versions.

• Partial document match - may locate files with partial matches, such as identical forms filled out by several people.

Statistical analysis - Bayesian analysis using machine learning algorithms can get used to finding content that breaks the rules or includes sensitive information. More labeled data sent to the algorithm during training can improve the efficiency of these strategies.

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