Data is an organization’s most valuable resource, and its security requires multiple protective strategies. One of the measures companies can take to safeguard their sensitive information is data loss prevention (DLP). It’s an essential component of a robust security posture that all organizations should consider implementing.
This guide discusses all aspects of data loss prevention. We’ll look at what DLP is and how it works. We’ll also examine the differences between legacy DLP tools and modern solutions that better address the volume of data companies need to protect, and the speed at which it is generated.
We’ll also identify the leading causes of data leaks and best practices that companies can follow to minimize their occurrence. Finally, we will talk about DLP solutions and the most important features to look for when selecting the right one for your enterprise.
Data loss prevention is a comprehensive strategy to protect an organization’s valuable data from internal and external threats. DLP combines multiple processes and services that work cooperatively to identify and secure enterprise data resources based on an organization’s defined data handling policy.
DLP solutions allow companies to identify their more sensitive and high-risk data so it can be given the additional protection it requires. Each company has a unique collection of data resources, all of which do not need the same level of security. Important and high-value data needs to be protected against various threats that include:
A DLP solution takes the necessary actions to prevent information from being misused according to the rules of the organizational data handling policy. Let’s take a deeper look at how DLP works to protect enterprise data and the benefits of implementing a data loss prevention solution.
Data loss prevention employs a multi-step process that identifies an organization’s sensitive information and enforces defined measures to prevent data leaks.
The creation of a data handling policy is a prerequisite to implementing a DLP solution. A company’s data handling policy reflects the type of information they process and store. As a result, the policy is necessarily different for each organization. The data handling policy defines the rules regarding how different types of data can be used, shared, and accessed by employees and external sources.
In addition to enterprise-defined rules, an organization’s data handling policy must incorporate any regulatory standards that apply to its data resources. Regulations such as HIPAA, PCI-DSS, and GDPR stipulate how certain types of personally identifiable information (PII) or protected health information (PHI) are handled to ensure its privacy and security.
Taking all these factors into account, the data handling policy is designed to categorize each data element and assign it to one of the following risk levels:
The purpose of defining these categories and assigning data elements to them is so information can be handled appropriately throughout the organization. In addition to its importance for data loss prevention, other protective measures can be influenced by how information is categorized. For instance, companies may perform more frequent backups of high-risk data and store it on hardened storage devices to provide enhanced security.
Data classification is performed based on a company’s data handling policy. All data elements within the computing environment need to be classified so they can be handled correctly. Traditionally, data needed to be pre-classified before it could be used by a DLP tool. Modern DLP solutions can classify data on-the-fly, as it is created, eliminating the process of pre-classification.
Data elements are classified using three different techniques which are often used in combination for more precise classification.
The heart of a DLP solution is its ability to enforce the company’s pre-defined data handling policies. Modern DLP solutions often come with pre-built policy packs or templates that simplify the creation of policies to address various compliance requirements and rules for handling different classes of data.
DLP automates the enforcement of data handling policies and remediates issues that occur. For example, a DLP solution will prohibit high-risk data from being transmitted in unencrypted form. Based on how the policy is defined, the tool may automatically encrypt the data and allow its transfer or completely block the transaction. Low-risk data does not need the same protection and can be allowed to be transferred at will without encryption.
A DLP solution is most effective when everyone in the organization understands the risks associated with insecure data handling. Cybersecurity awareness training is an important part of keeping a company’s data secure. Employees trained on the business risks of exposing sensitive information are more likely to take the necessary steps to protect it.
Modern DLP solutions offer real-time, incident-based security education that helps employees understand why a given action was prohibited and what they can do to avoid repeating it in the future. This type of training can greatly reduce inadvertent mistakes that can lead to data loss.
Reports generated from a DLP solution can be used to identify specific vulnerabilities and operational deficiencies that need to be addressed in the interest of securing data resources. The reports can be used in multiple ways.
Consistent violations of data handling policy by a given department or individual can highlight the need for additional training. If the violations continue after adequate training, it may be that a potentially malicious insider has been identified and a company can take the necessary disciplinary actions.
Reports may also indicate that false alerts and warnings are being generated by the DLP tool. Revisiting data classification policies may be in order to reduce the number of incorrect violations that are reported. Through analytics, an enterprise can identify where its high-risk data is primarily used and leverage this information to adopt additional cybersecurity measures.
The creation of a data handling policy and the subsequent enforcement of the rules by a DLP solution provides enterprises with multiple benefits.
If an organization intends to effectively protect its high-risk and sensitive data, it needs to know where it will be stored. This has become increasingly difficult with the rise of cloud and hybrid computing environments. Without an efficient DLP solution, it is virtually impossible to track the movement of high-risk data throughout an enterprise.
A DLP policy and associated software solution protect a company’s intellectual property from misuse, disclosure, or theft. The location of intellectual property should be apparent with the enhanced visibility provided by the DLP solution.
Regulatory compliance has become more important to a larger group of organizations due to the growth of ecommerce. Nearly every company with an online retail presence stores customer details that fall into the high-risk category, such as credit card details. Companies operating in the healthcare field also need to protect patients’ protected health information or risk substantial fines and reputational damage.
Malicious insiders pose a grave risk to enterprise data resources. Employees using stolen credentials or elevated privileges can gain access to high-risk data that can be used for financial gain or to damage the organization. A DLP solution will track and stop unauthorized attempts to access this information. In situations where the violations were found to be deliberate attempts to subvert company policy, disciplinary action can be taken.
One of the primary functions of DLP is to eliminate data leaks and protect an organization’s sensitive and high-risk information. Data leaks can be triggered in a wide variety of ways. Following are some of the most common causes of data leaks or breaches.
The following best practices can help minimize the potential for data leaks across the enterprise.
A modern DLP solution is a software tool that performs classification based on a company’s data handling policies. As it classifies data elements, the tool enforces the policy when it detects violations. It takes protective actions such as encrypting high-risk data or prohibiting its transfer.
Automating data classification and the enforcement of data handling policies guards against data leaks. A DLP solution also provides education to the people who are responsible for protecting enterprise data. Taken together, the benefits and features of a DLP solution offer companies an effective means of protecting their intellectual property and high-risk data.
DLP solutions are not all designed and created equally. Legacy tools are complex and require data to be pre-classified before handling policies can be enforced. The features we highlight below are what you should look for in a modern and efficient DLP solution.
Data loss prevention is a vital component of enterprise security and is essential in protecting a company’s high-risk and sensitive data assets. Next DLP offers its customers a modern approach to data loss prevention that encompasses all the features referenced above in an easy-to-implement and use solution called Reveal. Contact us to book a demo to learn how this innovative, human-centric DLP solution can help your business protect its valuable resources.
