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Data Loss Prevention(DLP) - What Is, Methods and Best Practices

Data Loss Prevention(DLP) - What Is, Methods and Best Practices

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 (Data Loss Prevention)

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:

  • Keep personally identifiable information (PII) secure and adhere to all applicable regulations.
  • It is critical that the organization safeguard its intellectual property.
  • Obtain data visibility in large organizations.
  • Secure mobile workforces and enforce security in Bring Your Own Device (BYOD) environments.
  • Data protection on remote cloud systems

Aim of DLP (Data Loss Prevention)

DLP aims to prevent unauthorized access to sensitive information by utilizing a variety of software tools and data privacy policies. This is accomplished by classifying the various content types within a data object and implementing automated protection policies.

Developing a DLP plan also allows a company to review and update its data storage and retention policies in order to maintain regulatory compliance.

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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:

Data Identification

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.

Protecting Data In Motion

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.

  • Protecting Data At Rest

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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  • Detection Of Data Leaks

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

followed by constantly searching for unusual behavior.

  • DLP Cloud

DLP solutions have evolved to manage and protect critical data

in Software as a service and Infrastructure as service applications.

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Data Loss Prevention Solutions

  • Securing Data In Transit

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

  • Endpoint Protection

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.

  • Data Security In Use

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 tools depend on two types of products: dedicated and integrated.

  • Dedicated products are in-depth and complex dedicated hardware products.
  • Integrated products are simpler, work with other security tools to enforce policies, and are less expensive than dedicated DLP tools.

It’s unlikely that a single tool will meet all of an organization’s data loss prevention requirements. Many DLP vendors specialize in one area, whereas others offer suites of tools that work together. Businesses can either put together a collection of the finest tools or use an all-in-one suite.

Data Loss Prevention Top Webshops

The following are some of the top Webshops:

  • Arcserve UDP

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.

  • Broadcom’s Symantec Data Loss Prevention

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

  • CoSoSys Endpoint Protector

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 Data Loss Prevention

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

  • McAfee Total Protection for DLP

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.

  • Solorwinds Data prevention

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.

  • Symantec Data Loss Prevention

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
  • Conduct an inventory and evaluation

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.

  • Classification of data

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.

  • Implement policies for data handling and rehabilitation

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.

  • Automating Systems

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.

  • Develop Metrics

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.

  • Make use of algorithms

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

  • Educating employees

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

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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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About the Author

Technical Lead - AWS Solutions Architect

Shivanshu is a Technical Lead and AWS Solutions Architect passionate about utilizing Cloud technology to empower businesses. Proficient in AWS, Terraform, and GCP, he crafts innovative solutions to propel companies forward. As an enthusiastic writer, he shares his expertise to inspire others in this field.