What Is DLP? A Comprehensive Guide to Data Loss Prevention

In this article, you’ll learn about Data Loss Prevention (DLP) and find out how it can become an excellent investment for your business.

Data is probably one of the most valuable assets of any company (along with its employees, of course). Organizations use their information resources to analyze customer sentiment, monitor market trends, and maintain a competitive edge.

The sheer volume of data generated nowadays by businesses through multiple channels, together with the ever-evolving types of data security threats, makes the task of protecting company data rather challenging.

According to Statista survey, the average cost of a corporate data breach in 2023 was a disturbing $9.48 billion.

No wonder companies tend to struggle to protect critical information, such as its intellectual property or personal employee or client information. That’s why data loss prevention is rapidly becoming oh so crucial for a company of any size.

But what exactly is DLP, and how to implement it properly? In today’s article, we’ll discuss the definition of DLP and its importance for business strategies, look at how it works, and review some of the best practices you should consider to ensure the successful implementation of your data loss prevention solution. Keep reading to discover more about how you can protect your valuable company data.

What Is Data Loss Prevention (DLP)?

Data Loss Prevention (DLP) is a comprehensive approach to protecting sensitive company information from external and internal threats.

DLP is a compilation of processes and tools working together to ensure that an organization’s sensitive data is not lost, mishandled, or accessed by unauthorized users.

DLP solutions classify data in real time and look for violations of an organization’s data handling policies. If violations are discovered, DLP helps to remediate the issue by implementing protective actions (such as, let’s say, clearing the clipboard when a user copies data from unauthorized apps) and issuing alerts to the cybersecurity specialists so they can quickly respond to the incident. Certain DLP software can also even generate reports to demonstrate cybersecurity compliance and provide evidence in case of any disturbances to data security.

Why Is Data Loss Prevention Important?

It’s rather obvious that every business needs to ensure protection of its intellectual property and sensitive information from negligent or malicious actions by both internal and external threats.

Here are some of the main reasons why it would be wise for companies to use DLP:

• Protection of personal information
  Businesses are subject to mandatory compliance standards that governments impose on them. Companies collect and store Personally Identifiable Information (PII), Protected Health Information (PHI), as well as payment card information of both their employees and clients. And since mandatory compliance standards require companies to protect such sensitive information, data loss prevention becomes an immense help as it can identify, classify, and tag sensitive data and monitor activities around that data.
• Protection of intellectual property
  Every company owns intellectual property and strategically valuable information that must not be leaked under any circumstances. Losing this information could potentially damage the company’s finances as well as tarnish its good name and reputation. A DLP tool can help with identification and protection of said information from undesired infiltration.
• Data visibility
  To be able to protect sensitive information, a company should know where the data is stored, as well as what users and for what purposes have access to it. To increase the visibility of data movements, organizations can implement efficient DLP software that will help identify weak points and eliminate unnecessary risks.

What Are the Main Types of Data Threats?

There is a variety of methods that can be used to steal data from an organization. Some of the most common types of data threats include the following three:

• Data Exfiltration. This method refers to an unauthorized transfer of sensitive information from a company’s network to an external location (to put it in more simple terms, stealing confidential data). It uses techniques like phishing and different malware attacks. Data exfiltration can be both intentional, as in the case of a malicious insider looking to profit from sensitive company data, and accidental, such as when an employee unknowingly sends a sensitive file to the wrong recipient.
• Insider Threats. This type of data threat happens when a current or former employee use their access to move sensitive data outside the organization. Contractors, partners, or even clients a who have been granted access privileges can become insider threats as well. Insider threats can originate from malicious intent, such as employees stealing sensitive data for personal gain or as a way to seek revenge, or from inadvertent actions, such as unintentional sharing of confidential information.
• Negligence. Unintentional negligent data exposure is also considered a type of data threat. Situations where sensitive data gets leaked or exposed due to negligence, human error, poor cybersecurity training, or lack of awareness can result in serious consequences, including financial loss, reputational damage and legal liabilities.

How Does DLP Work?

With the volume of data companies generate nowadays, the idea of pre-discovery and pre-classification of all data seems like a dream.

In broad terms, company data can be classified into three categories:

• Low-risk data: publicly available information and data that can be recovered or recreated easily.
• Moderate-risk data: internal data that is important to the company but does not meet the criteria of high-risk data.
• High-risk data: confidential and sensitive data that should not under any circumstances be disclosed, or critical data that cannot be recreated or recovered easily.

To classify data, three basic methods are generally used:

• Content-based classification: automation is used to search files for sensitive information.
• Context-based classification: indirect indicators, which may include the information’s location, its creator, or the app that used the data, are used to classify data.
• User-based classification: data sensitivity is established manually using user knowledge.

In the simplest terms, a DLP solution identifies sensitive company data and protects it. DLP solutions focus on different data state, such as:

• Data in use: The data stored in cache memory, CPU registers, and RAM.
• Data in motion: The data transmitted from the internal to the public network (and vice versa).
• Data in rest: The data stored in the database, backup storage, or on a file system.

If the DLP solution focuses on just one of these data states, it is considered an integrated DLP solution. A good example is Microsoft Exchange Server, which has DLP capabilities to prevent data loss via email and doesn’t focus on other data states).

If the focus of the DLP tool is spread across all the data states, it’s called an enterprise DLP solution. This DLP technology is rather extensive and usually comes in the form of software for desktops and servers, or physical/virtual appliances for network protection.

There are three types of DLP solutions, all of which have the same objective of preventing data loss, but with rather key differences in the techniques used to achieve this objective. DLP categories include:

• Network DLP
  Network DLP is used to monitor and protect data (at rest, in use, or in motion) on the company’s servers. It analyzes data traffic on the cloud and on traditional network systems to identify any violation of internal security policies. This type of DLP monitors file uploads and transfers, emails and company network messaging.
  If a user tries to gain authorized access to sensitive information on the company servers, network DLP will initiate predefined steps to prevent said user from accessing the data. Admins can view w. This It also provides increased visibility into ho accessed the sensitive data, when it was accessed and whether it was transferred, which helps mitigate the risk of data loss on the network.
• Endpoint DLP
  Endpoint DLP is aimed at protecting data that is in transit or in motion. It’s designed to monitor the endpoints of the network (e.g., computers, mobile phones, virtual desktops, USB storage, cloud repositories, and other devices that are connected to the network).
  Compared to network DLP, endpoint DLP does offer more extensive security. However, it also requires more management, from installing DLP tools on all devices that need to be protected to ensuring that these DLP solutions are maintained with their regular updates.
• Cloud DLP
  Cloud DLP is designed to protect data in the cloud. It scans and audits data residing on cloud repositories (e.g. Google Drive, Office 365 email) and automatically flags anomalies that require attention. It also maintains a log of times when sensitive data was accessed and users who accessed it, as well as a list of authorized cloud devices and users that have been granted corresponding access rights to said data.
  As the name suggests, rather than building a perimeter around the network, cloud DLP interacts with cloud applications to encrypt data.

Essentially, you need to deploy the right DLP solution that delivers an analysis of data in motion, real-time monitoring, accuracy, incident remediation, and different data loss policies that strengthen the security of your sensitive data.

What Are the Benefits of Using a DLP Solution?

DLP solutions can become incredibly helpful in a variety of cases, including:

• Ensuring compliance with regulations such as GDPR or HIPAA – DLP can help companies identify and classify sensitive information, add required security controls, as well as configure monitoring and reporting to protect the data.
• Preventing data leakage from endpoints – DLP can protect data stored on user endpoints such as mobile devices and laptops by identifying suspicious events and alerting cybersecurity teams of a risk of data loss.
• Data discovery – DLP can continuously discover and classify company’s sensitive data, no matter where it’s stored, as well as provide visibility into who is using the data and what exactly they are doing with it.
• Preventing data exfiltration – DLP can prevent data exfiltration in the event of a data breach by identifying a suspicious data transfer, blocking it, and alerting cybersecurity specialists.
• Providing central management of sensitive data – DLP can provide central control over all sensitive data assets, enabling managers to set policies, grant or revoke access to certain data, and generate compliance reports.

Now that you have gotten the hang of DLP solutions and how the software works, it’s probably time to take some action.

What Are Data Loss Prevention Best Practices?

1. Identify and monitor sensitive data
   For any DLP solution, the first step is to perform data inventory. Businesses ought to identify and classify sensitive data so they know exactly what type of data they have and what they’d need in order to protect it. A great way to do so is to use a data discovery technology that scans data repositories and generates a report on the type of data. Companies must identify the type of sensitive data they collect, where it is stored, and how it is used by employees. Data loss prevention software includes predefined profiles for sensitive data and also allows organizations to define new profiles according to their own specific needs.
2. Automate DLP processes
   Automation allows companies to offload repetitive and recurring tasks, as well as helps with a broader DLP implementation across the network. While it’s important to introduce manual DLP processes during the initial setup of the software to help configure it according to the specific needs of the organization, automating these processes helps maximize the scope of DLP implementation as a whole.
3. Use data encryption
   All business-critical data should be encrypted (including data at rest and in motion). Introducing data encryption, organizations get an added layer of defense against cyber attacks. Even if an intruder manages to gain access, the encryption helps keep data safe.
4. Define user roles
   Determine the level of access different users in your organization require and configure these rules with the help of DLP tools. User roles should clearly define the responsibilities of different users (including the stakeholder roles in case data loss occurs).
5. Establish data security policies and examine them
   To keep the sensitive data identified safe, DLP tools offer a wide array of pre-configured rules and security policies that can be enforced across the company network. These policies may include blocking confidential information or private employee data from being transferred via possibly unsecured channels, such as cloud storage services, messaging apps, or file-sharing services.
6. Educate employees on DLP
   Well-structured DLP software addresses a variety of data protection challenges. It’s equally important for companies to educate employees on the topic of internal cyber security policies to ensure their compliance with DLP procedures and acceptance of this new measure. This pragmatic approach enhances overall data security and minimizes numerous data security risks for organizations. DLP awareness training may include activities such as in-person classes, online training, email lists, written reports enhancing employees’ understanding of the topic, etc.

What DLP Tool to Choose?

According to Statista projection, the DLP market will experience almost a 65% increase, growing from $1.24 billion in 2019 to $3.5 billion by 2025.

With many different DLP solutions available out there in the market, it might be hard to navigate through all possible choices. Luckily, we’ve already compiled an overview of the best data loss prevention tools for your convenience. Let’s have a quick look at some of the most popular DLP solutions once more.

1. Kickidler DLP
   Kickidler DLP is a comprehensive data loss prevention solution that provides organizations with immediate benefits. It provides DLP functionality with full visibility. It employs lightweight agents that detect data exfiltration attempts and help detect risks before they become security incidents. It’s a flexible solution, compatible with Windows, macOS, and Linux machines.
2. Forcepoint
   Forcepoint is a solution that works on-premise and in the cloud and is aimed at preventing sensitive data from exfiltration. The product claims a data-first approach to cybersecurity, and it’s overall considered a strong tool with rich functionality.
3. Digital Guardian
   Digital Guardian is a SaaS DLP powered by AWS that is known for a number of interesting features, such as automated data discovery, granular policies for better protection of sensitive data, and its high scalability.

Secure Your Sensitive Data Today with the Help of Kickidler!

Now that you got a grip on how DLP works, you have taken the first step towards preventing sensitive data breaches on your company network.

To implement a robust DLP solution, you need to analyze different business security requirements and point out the security loopholes relevant to your particular case.

Analyzing different data states using various features of DLP software can help you monitor and shield your company’s sensitive data from being leaked or exploited by hackers and unwanted intruders.

Get in touch with us here at Kickidler and learn how you can protect your company valuable data with our efficient DLP solution.