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What is DLP (Data Loss Prevention)?

What is Data Loss Prevention?

DLP Stands for Data Loss Prevention. Data Loss Prevention is a security risk strategy for critical data, known as data leak prevention, information loss prevention, and extrusion prevention. DLP is frequently used as a company’s overall data security strategy.

In this blog we will be going through the following topics:

Table Of Contents:

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All About DLP

The term DLP refers to defending companies against both data loss and data leakage prevention. Data loss is an event in which critical data is lost to the organization, such as in a ransomware attack. Data loss prevention focuses on preventing the unauthorized transfer of data outside of organizational boundaries.

DLP is commonly used in businesses to:

Aim of DLP

Data Loss Prevention Methods

Data Loss Prevention Methods

Network DLP includes a wide range of data security methods. Some of these examples are listed below:

DLP is only useful if you tell it what is sensitive and what is not. Rather than relying on humans, organizations should use automated data discovery and classification tools to ensure reliable and accurate data identification and classification.

Internally, data is moved around a lot, and external breaches frequently rely on this to redirect data. DLP software can help ensure that data in transit does not end up somewhere it should not be present.

This technique secures data when it is not in motion, such as when it is stored in databases, other apps, cloud folders, computers, mobile devices, and other storage devices.

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Setting a baseline of normal activity is the first step in this technique,

followed by constantly searching for unusual behavior.

DLP solutions have evolved to manage and protect critical data

in Software as a service and Infrastructure as service applications.

Data Loss Prevention Solutions

edge of the network technology can analyze traffic to detect sensitive data sent in violation of security policies.

endpoint-based agents can control data transfer between users, groups of users, and third parties. Some endpoint-based systems can detect and respond to attempted communications in real-time.

Some DLP systems can monitor and flag unauthorized activities that users may perform intentionally or unintentionally in their interactions with data.

Data Loss Prevention Software Tools

Data Loss Prevention Top Webshops

The following are some of the top Webshops:

This tool is used toPrevent data loss and downtime in the cloud and on-premise workloads and Validation of data recovery for shorter downtimes. Storage optimization by releasing 20 times more capacity.

Data points, data centers, and cloud computing are all covered by this enterprise-level DLP software.

A selection of onsite or cloud-based data loss prevention systems that protect Windows, macOS, and Linux devices, as well as attached storage devices.

Google Cloud DLP is intended for advanced Google Cloud users who want to improve data asset strength and scale their cloud-based infrastructure.

Manages to combine this security giant’s premier data security offerings. With cloud-based online and offline protection, you can defend against viruses, online threats, and ransomware.

Set up an access rights manager to help protect against accidental or intentional data loss. Can use policy to automate user access and activities, respond to suspicious activity, and investigate user events that may compromise your systems.

From a single console, this system provides data protection solutions for endpoints, networks, cloud resources, and file servers. Installs on Microsoft Windows Server and Linux.

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Data Loss Prevention Best Practices

Organizations can implement a DLP system in several ways, including the following:

Data Loss Prevention Best Practices

Businesses cannot protect what they are unaware of. A complete inventory is required. Some DLP products, such as those from Malware Networks, Cisco, and McAfee, will perform a full network scan on their behalf.

Data should be classified. A data classification framework is required for both structured and unstructured data in organizations. Personal identifiable information (PII), financial data, regulatory data, and intellectual property are examples of such categories.

After classifying the data, the next step is to develop policies for handling it. This is especially true for regulated data or areas with strict rules, such as Europe’s GDPR and California’s CCPA.

The more DLP processes that are automated, the more widely they can be deployed across the organization. Manual DLP processes are inherently limited in scope and cannot scale to meet the needs of all but the most rudimentary IT environments.

Metrics such as the percentage of false positives, the number of incidents, and the mean time to incident response can be used to examine the performance of your DLP strategy.

Some modern DLP solutions support simple statistical analysis and correlation rules with machine learning and behavioral analytics to detect abnormal user behavior.

Unintended implications far outnumber malicious intent. DLP depends heavily on employee awareness and acceptance of security policies and procedures.


Financial data, private information, credit card numbers, health records, and social security numbers are all instances of sensitive information under an organization’s control. They require a means to prevent their users from sharing sensitive data with people who should not have it in order to help protect it and reduce risk. This is why DLP is needed.

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