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Data Loss Prevention Best Practices: How to Protect Your Company’s Sensitive Data

by Chris Brook on Tuesday July 11, 2023

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Most companies understand the importance of data protection but don't always know how to implement an effective data loss prevention program. Here's a quick primer.

In today’s highly-competitive business landscape, a company’s data resources are one of its most valuable assets. A business needs to take every precaution to ensure its data is kept secure from external and internal threats. The cost of a data breach in the U.S. averages $9.44 million, representing a substantial sum of money that most companies are not in a position to lose.

While most companies understand the importance of data protection, they don’t always know how to implement an effective data loss prevention program. Failing to follow best practices can leave your company vulnerable to attack and put your sensitive data at risk. 

In this article, we’ll discuss the essential best practices for data loss prevention to help you protect your company’s valuable data. 

What is Data Loss Prevention? 

Data loss prevention (DLP) is a comprehensive strategy that can be instrumental in protecting a company’s sensitive and important information from external and internal risks. DLP combines processes and services that work together to identify and secure an organization’s data resources.

With a DLP solution in place, companies can be certain that data is handled appropriately by everyone in the organization. 

DLP protects against many threats to valuable information, including:

  • Compromised data resources that have been corrupted
  • Data theft carried out by internal or external actors
  • Lost or misplaced data that cannot be accessed efficiently when needed
  • Unauthorized access to sensitive data by individuals within and outside of the organization

The following best practices will help guide a company that has chosen to implement data loss prevention as part of its overall cybersecurity posture.

Establish a Data Handling Policy

The main purpose of a DLP solution is to enforce an organization’s data handling policy. While there are general aspects of data loss prevention that can be implemented out of the box, a data handling policy codifies a company’s unique requirement regarding how sensitive and high-value information is treated across the organization. 

The policy needs to incorporate company-specific rules as well as those that may be required to address dilatory standards like PCI-DSS or HIPAA.

The objective of a data handling policy is to firmly document how every data element is categorized so it can be afforded the protection it warrants. 

At a minimum, data should be designated as belonging to one of three categories:

  • High-risk data - This category is reserved for an organization’s most sensitive, important, and valuable data. Information in this category includes confidential and personal information on customers and employees. Data subject to regulatory guidelines, business-critical information, and intellectual property also fall into this category.
  • Moderate-risk data - Data in this category holds some value to a company but does not pose the same level of risk if disclosed. It does not require the same degree of security as high-risk data.
  • Low-risk data - Information in this category is publicly available or encompasses data that would not cause damage if lost.

Data Discovery and Classification

Following the creation of a data handling policy, an organization needs to identify and classify its data assets. Fortunately, modern DLP tools do not require data to be pre-classified and are capable of dynamically classifying data as it is created or ingested. 

Classification is performed with three complementary methods:

  • Content-based classification is an automated process that searches files and categorizes them based on their content.
  • Context-based classification automatically classifies data according to indirect indicators such as location, creator, and users.
  • User-based classification relies on user knowledge to classify a data element and is used to complement content and context-based classification.

Enforcing the Data Handling Policies

The main purpose of a DLP tool is the effective enforcement of a company’s data handling policies. Some tools come with templates that offer generic methods with which to address data resources that are used in ways prohibited by the data handling policies. An organization typically customizes these methods and procedures to align with its business objectives.

Common data handling policy enforcement activities might include:

  • Encrypting all high-risk data before it is transmitted
  • Disabling a user’s ability to print sensitive information on unsecured printers
  • Stopping unauthorized users from accessing high-risk data

Providing User Education and Training

The human element is a vital component of effective cybersecurity. Everyone in the organization should understand the importance of abiding by the data handling policy and their role in protecting company information. Offering continuing cybersecurity training and education is necessary to ensure employees stay up to date with the evolving methods used by threat actors to compromise data resources.

Use Reporting and Analysis Capabilities

DLP solutions typically offer the capability to generate reports that can identify individuals or departments that consistently violate data handling policies. This information can be used to address possible vulnerabilities or as an impetus for additional training.

Employ a Modern and Reliable DLP Tool

Digital Guardian offers companies a SaaS-based, modern approach to DLP. The solution can be quickly deployed while providing full visibility into data resources. It’s a cross-platform solution that supports Windows, macOS, and Linux systems and endpoints. The tool allows administrators fine-grained controls to effectively enforce data handling policies while protecting a company’s valuable information.

When a user attempts to access data, Digital Guardian acts based on the classification of a data element, the context of the action, and the applicable policy to determine enforcement activities. From discovery to monitoring to blocking, Digital Guardian's comprehensive data loss prevention capabilities help support compliance initiatives and keeps your valuable information from getting outside your network.

Get in touch with Digital Guardian and schedule a free demo that illustrates the functionality of its DLP tool and how it can benefit your company. 


Tags:  Data Loss Prevention

Chris Brook

Chris Brook

Chris Brook is the editor of Digital Guardian’s Data Insider blog. He is a cybersecurity writer with nearly 15 years of experience reporting and writing about information security, attending infosec conferences like Black Hat and RSA, and interviewing hackers and security researchers. Prior to joining Digital Guardian–acquired by Fortra in 2021–he helped launch Threatpost, an independent news site that was a leading source of information about IT and business security for hundreds of thousands of professionals worldwide.

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