{"id":14127,"date":"2022-08-31T00:00:00","date_gmt":"2022-08-31T07:00:00","guid":{"rendered":"https:\/\/www.syteca.com\/blog\/en-blog-dlp-systems-pros-and-cons\/"},"modified":"2024-06-19T05:41:46","modified_gmt":"2024-06-19T12:41:46","slug":"dlp-systems-pros-and-cons","status":"publish","type":"post","link":"https:\/\/www.syteca.com\/en\/blog\/dlp-systems-pros-and-cons","title":{"rendered":"Data Loss Prevention (DLP) Systems: Types, Key Features, Pros and Cons"},"content":{"rendered":"\n

As the number<\/a> and severity<\/a> of cybersecurity attacks rise each year, organizations are compelled to look for measures to protect sensitive data. The abundance of cybersecurity solutions on the market may create confusion and pressure, as choosing the wrong one may lead to security gaps. Many companies turn to data loss prevention (DLP) systems, since they have been on the market for years.<\/p>\n\n\n\n

But is a DLP system enough to protect your data? Can we consider these time-tested solutions a panacea against all cybersecurity incidents? Stay with us to find out. In this article, we outline the main pros and cons of DLP systems and explain how to cover their potential security flaws if you decide to deploy one.<\/p>\n\n\n\n

What is a data loss prevention (DLP) system?<\/h2>\n\n\n\n

Data loss prevention<\/a>, or data leak prevention, is the process of using different tools and practices to mitigate the risks of data-related security incidents. Data loss prevention benefits organizations by helping them detect and prevent sensitive data loss, leaks, exfiltration, and breaches.<\/p>\n\n\n\n

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Data loss prevention (DLP) describes a set of technologies and inspection techniques used to classify information content contained within an object \u2014 such as a file, email, packet, application, or data store \u2014 while at rest (in storage), in use (during an operation) or in transit (across a network).<\/p>\nGartner<\/cite><\/blockquote>\n\n\n\n

A DLP system<\/strong>, in turn, is a software solution or a set of tools that can help prevent the loss of data. DLP systems monitor sensitive data to detect and block suspicious operations with it inside an organization\u2019s infrastructure.<\/p>\n\n\n\n

Frequently implemented to combat insider threats, DLP solutions are also a requirement<\/a> of many industry security standards, laws, and regulations, such as GLBA<\/a>, DSS<\/a>, SOX<\/a>, and FISMA<\/a>.<\/p>\n\n\n\n

DLP solutions can help prevent the following types of data security incidents:<\/p>\n\n\n\n

\"Data-related<\/figure>\n\n\n\n

<\/p>\n\n\n\n

A data breach<\/strong><\/a> is a security incident involving unauthorized third-party access to an organization\u2019s critical data. Cybercriminals typically breach an organization\u2019s security to sell sensitive data. Data breaches can be carried out using various methods such as social engineering, malware, and hacking.<\/p>\n\n\n\n

Data exfiltratio<\/strong><\/a>n<\/strong><\/a> is the unauthorized and intentional transfer of critical data out of an organization\u2019s perimeter. Also referred to as data theft, exfiltration is mostly done by malicious insiders acting as authorized employees.<\/p>\n\n\n\n

A d<\/strong><\/a>ata leak<\/strong><\/a>, or data leakage, is an unauthorized but unintentional transmission of sensitive data from an organization\u2019s perimeter. Leaked sensitive data may be exposed publicly or used maliciously by third parties to perform a cybercrime such as identity theft.<\/p>\n\n\n\n

Data loss<\/strong> is any event or process that leads to data being corrupted, damaged, or completely destroyed, and therefore inaccessible or unusable.<\/p>\n\n\n\n

Note: The above data security categories are similar and frequently overlap. Some classifications take data loss<\/em> as an umbrella term covering all of the above-mentioned data security incidents. Note: The above data security categories are similar and frequently overlap. Some classifications take data loss as an umbrella term covering all of the above-mentioned data security incidents.<\/p>\n\n\n\n

DLP systems may consist of various software means to protect sensitive data. Standard measures include firewalls, intrusion detection systems, and antivirus software, which mainly focus on external attacks that may cause data loss. More advanced measures comprise machine learning, user activity monitoring, and other technologies to detect unauthorized access and malicious insider activity.<\/p>\n\n\n\n

In our analysis of DLP systems, we focus on those specifically designed to prevent unauthorized attempts to copy, modify, delete, or send sensitive data.<\/p>\n\n\n\n

Auditing and Reporting with Syteca<\/a><\/p>\n\n\n\n

How does a DLP system work?<\/h3>\n\n\n\n

To understand how DLP solutions operate, let\u2019s take a closer look at their core workflow. Each organization\u2019s DLP system is slightly different; however, all of them share similar key steps:<\/p>\n\n\n\n

\"Data<\/figure>\n\n\n\n

<\/p>\n\n\n\n

1. Classifying data. <\/strong>Cybersecurity experts use a DLP solution to sort and label every piece of data in an organization\u2019s infrastructure with a particular classification level, such as public, sensitive, or internal. Classification is a lengthy and complex process that requires the development of data classification policies<\/a>. It may be manual or automated, depending on the DLP solution in place.<\/p>\n\n\n\n

2. Determining confidentiality levels.<\/strong> Based on the classification results, security officers assign specific phrases indicating confidentiality levels to different types of data: private, confidential, top secret, credit card information, etc. Depending on the company, there could be hundreds or even thousands of confidentiality markers.<\/p>\n\n\n\n

3. Developing security rules.<\/strong> Once all of the organization\u2019s data is processed, a cybersecurity team configures the DLP system\u2019s behavior by creating rules. These rules define how the system responds when a specific confidentiality marker is triggered: by sending an alert notification, stopping transmission of confidential data, or revoking the user\u2019s access rights.<\/p>\n\n\n\n

4. Monitoring and investigating.<\/strong> The system begins monitoring sensitive data after the security rules are set up. When an alert is triggered, the system responds to the event as configured and notifies the cybersecurity team. Security personnel then analyze the event to see if it is an incident or a false positive.<\/p>\n\n\n\n

5. Classifying new data.<\/strong> As fresh data enters the organization\u2019s infrastructure, the DLP process starts all over again, beginning with the classification step. Some DLP solutions can offer a certain level of automation, but it\u2019s not flawless \u2013 it may leave gaps in security and affect your organization\u2019s workflow.<\/p>\n\n\n\n

Types of DLP systems<\/h3>\n\n\n\n

Depending on the environment they operate in, we can distinguish the following types of DLP solutions:<\/p>\n\n\n\n

\"Types<\/figure>\n\n\n\n

Key DLP system features<\/h3>\n\n\n\n

The technical capabilities of modern DLP systems vary, but certain DLP solution features are critical to thoroughly protect data. Here are the key features DLP systems should have:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Data identification<\/h4>\n\n\n\n

A DLP system needs to determine what data requires protection the most. This can be determined manually or automatically. DLP systems either identify sensitive data with the help of machine learning or enable users to define sensitive data manually by applying rules or categories.<\/p>\n\n\n\n

Protection of data at rest<\/h4>\n\n\n\n

DLP systems can protect data located in various types of storage within your network, including databases, software applications, cloud repositories, computers, and mobile devices.<\/p>\n\n\n\n

Protection of data in motion<\/h4>\n\n\n\n

Malicious actors may exploit your organization\u2019s data movements to reroute data. DLP systems can protect data in motion and detect cases when data is moved in violation of data security policies.<\/p>\n\n\n\n

Data leak detection<\/h4>\n\n\n\n

DLP solutions have the ability to identify anomalous or suspicious actions performed with the organization\u2019s data and alert security officers about possible data leaks.<\/p>\n\n\n\n

Now that you know more about DLP systems, let\u2019s consider their pros and cons.<\/p>\n\n\n\n

Advantages and disadvantages of DLP systems<\/h2>\n\n\n\n

This section will help you determine whether DLP solutions are worth your attention. By comparing the main advantages and disadvantages of DLP systems, you\u2019ll be able to make the right decisions about protecting sensitive data in your organization.<\/p>\n\n\n\n

Take a look at our key findings:<\/p>\n\n\n\n

\"DLP<\/figure>\n\n\n\n

Let\u2019s explore these pros and cons of DLP systems in greater depth.<\/p>\n\n\n\n

Key benefits of DLP systems<\/h3>\n\n\n\n

A DLP solution benefits your cybersecurity system and enables your organization to:<\/p>\n\n\n\n