Explain Data Loss Prevention and How Does it Work?

DLP can be defined as technologies that perform content inspection and contextual analysis of data while it is in transit over the network. It is used on a managed endpoint device while stored in on-premises file servers or rested on them. These solutions act in reaction to stated policies and procedures, mitigating the potential for sensitive information to be leaked or exposed outside of approved channels.

Enterprise DLP and Integrated DLP are the two leading families of DLP tools.

How it Works?

To fully grasp any DLP solution, it is crucial to understand the distinction between content awareness and contextual analysis. If the content is a letter, then the context is the envelope. Context refers to everything outside the letter, such as the header, size, format, etc. In contrast, content awareness refers to grabbing the envelope and gazing inside it to assess its contents. The concept of "content awareness" stems from the realization. However, using context to learn more about the content is desirable. It is also undesirable to be limited to a single context.

Once the envelope has been opened and its contents examined, several content analysis methods may be employed to detect policy breaches, such as

Rule-Based/Regular Expressions − Data loss prevention (DLP) often uses a rule engine to examine data for specific patterns, such as the existence of a credit card number (16 digits) or a social security number (9 digits) in the United States. Due to the ease with which rules may be written and executed. This method makes for a tremendous first-pass filter. However, it is prone to a high percentage of false positives without checksum checking to distinguish between valid and invalid patterns.

Database Fingerprinting − Sometimes called Exact Data Matching, it is a technique that compares data in a database dump with data in a running database to find matches. However, this may be done with structured data from databases, even though the speed suffers with dumps or live database connections.

Exact File Matching &mimnus; It works without content analysis, using a system that compares their hashes to known good fingerprints. It offers a low rate of false positives but fails when applied to files that exist in numerous comparable but distinct versions.

Partial Document Matching − It involves the search for a complete or partial match on selected files.

Conceptual/Lexicon − These policies may detect entirely unstructured thoughts that defy easy classification by utilizing dictionaries, rules, etc. The given DLP solution requires some tweaking.

Statistical Analysis − To find evidence of policy violations in encrypted data, it uses machine learning or other statistical techniques, such as Bayesian analysis. A significant amount of data is necessary to avoid producing false positives and negatives.

Pre-built categories − Credit card numbers/PCI protection, health information/HIPAA, etc. are just a few examples of sensitive information already accounted for in pre-built categories, including rules and dictionaries.

Numerous methods exist now that can do various forms of content analysis, and they may be found on the market. Some DLP providers use third-party technology that wasn't built for DLP, which is something to keep in mind, given that many DLP companies have built their content engines.

Best DLP Practices to Protect Sensitive Data

Technology, process controls, experienced personnel, and employee awareness play a role in what constitutes best practices in DLP. The following are some suggestions for creating a successful DLP program

Establish a Unified DLP Program − Currently, many companies use disparate DLP methods and technologies that are implemented independently by different divisions and departments. Due to this discrepancy, data security is compromised, and data assets cannot be seen. Furthermore, employees tend to respect departmental DLP initiatives that need more backing from upper management.

Take Stock of your Existing Assets − To develop and implement a DLP strategy, your company will require the services of individuals knowledgeable in areas such as DLP risk analysis, data breach response and reporting, data protection regulations, and DLP training and awareness. While complying with some government rules, businesses must train existing employees or hire outside experts with expertise in data protection. Companies that target customers in the European Union (EU) or collect data on their activities, like market researchers, are subject to specific requirements outlined in the General Data Protection Regulation (GDPR). A data protection officer (DPO) or staff that can assume DPO responsibilities is required by the General Data Protection Regulation (GDPR).

Make an Inventory and Analysis − Before starting a DLP program, it's vital to examine the different kinds of data and determine how valuable they are to the business. Data must be identified, where it is held, and whether or not it is considered sensitive (i.e., intellectual property, confidential information, or data covered by rules).

DLP is a Long-Term Process − It's ideal for rolling it out in stages. The best method is to arrange data and communication channels in order of importance. Similarly, it may be preferable to roll out DLP software in phases rather than all at once, depending on the needs of the business. Setting these priorities is aided by risk analysis and data inventory.

Create a Classification System − Develop a taxonomy or categorization framework for your organization's unstructured and structured data before establishing and implementing DLP regulations. Information can be classified as either secret, internal, public, personally identifiable information (PII), financial, regulated, intellectual property, or another category

Data Handling and Remediation Policies − The next step after developing a categorization framework is establishing (or revising) policies for dealing with various data types. Government regulations prescribe DLP procedures for dealing with sensitive data. DLP systems frequently apply pre-configured rules or policies based on legislation like HIPAA or GDPR. The DLP team may then adjust the guidelines to suit the business better.

Educate Workers − DLP relies on the workforce's widespread understanding and adoption of security policies and procedures. Classes, online training, monthly emails, videos, and written communications may help employees learn about the value of data protection and how to use the best DLP practices. Compliance might be boosted by enforcing stricter penalties for those who compromise sensitive information. Information security education and awareness materials are available from the SANS Institute.


Your company may use specialized DLP technologies or solutions to ensure the safety of its data. The Security Operations Center (SOC) provides additional resources to aid data loss prevention (DLP). For instance, a Security Information and Event Management (SIEM) system can monitor and analyze activity that could indicate a data breach.