The expert's guide to data loss prevention (DLP)

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.

What is data loss prevention? 

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:

• Lost data that cannot be accessed when needed
• Compromised data that may have been maliciously modified
• Data that has been stolen by external or internal actors
• Unauthorized access that can lead to any of the previously noted threats

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.

How does data loss prevention work? 

Data loss prevention employs a multi-step process that identifies an organization’s sensitive information and enforces defined measures to prevent data leaks.

Creating a data handling policy

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:

• Low-risk data - This is information that, if lost, would not cause harm to the organization. It includes publicly available data and information that can be recreated or recovered with minimal difficulty.

• Moderate-risk data - Data classified as moderate-risk is of some value to an organization but is not considered high-risk. This data can include items like internal operation guides and procedures that may cause some damage if leaked but do not pose a serious threat to the company.

• High-risk data - Data in this category is sensitive and confidential information that should not be disclosed or shared with unauthorized personnel. It includes data subject to regulatory guidelines and business-critical information that would be difficult to impossible to recreate or recover. High-risk data also includes intellectual property, which is unstructured data that could damage a company from a competitive standpoint if disclosed. 

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

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.

• Content-based classification is an automated process in which files are searched and categorized based on the type of information they contain.

• Context-based classification is also automated and classifies data based on indirect indicators such as where the information is located, how it was created, or the applications that use it.

• User-based classification leverages user knowledge to determine the risk level of specific data elements. This is strictly a manual process that can supplement automated content and context-based classification.

Enforcing data handling policies

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.

Providing continuing user education

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.

Reporting and analysis

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 benefits of data loss prevention 

The creation of a data handling policy and the subsequent enforcement of the rules by a DLP solution provides enterprises with multiple benefits.

Improved data visibility

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.

Protecting intellectual property

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.

Ensuring regulatory compliance

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.

Minimizing the threat of malicious insiders

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.

What are the main causes of data leaks? 

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.

• Weak passwords - Weak or trivial passwords open the door for hackers or malicious insiders to gain access to high-risk data.

• Cyberattacks - Hackers are continuously evolving their methods and trying to break into your systems to steal data or deliver malware.

• Application vulnerabilities - Unpatched security vulnerabilities are one of the most common ways that malicious actors can gain access to high-risk data, and we predict this will remain a trend in 2023. There may be backdoors known to the hacking community that have been inadvertently left open by an application’s developers.

• User error - User error often contributes to a data breach. Not following data handling policies, using weak passwords, and sending unencrypted information in emails can all result in a data leak.

• Malicious insiders - Unfortunately, malicious insiders also pose a threat to enterprise data resources. Employees may decide to exfiltrate data for financial gain or as retribution for real or imagined wrongdoings by management.  

• Phishing and social engineering- Phishing or tricking users into divulging credentials or sensitive information is a big problem with organizations and individuals. The only defense against this threat is the proper identification of phishing attempts so they can be thwarted.

• Malware and ransomware - Cyberattacks and phishing expeditions are often used to plant malware and ransomware into an organization’s computing environment.

• Incorrectly configured security settings - Configuration mistakes that affect system security can result in disastrous data leaks. This problem is especially prevalent in companies that have recently migrated to the cloud and may not have the expertise required to securely configure their systems.

Best practices for preventing data leakage

The following best practices can help minimize the potential for data leaks across the enterprise.

• Institute a strong password policy - A policy that rejects trivial and easily guessed passwords should be implemented and enforced throughout the organization. Compromising a single user’s credentials may allow hackers to gain access to business-critical and sensitive data.

• Timely patching - All systems should have the most recent patches installed as soon as possible. This is especially true for security patches. Patches usually indicate vulnerabilities that have been addressed by the developers. Companies that fail to patch their systems invite hackers to exploit those vulnerabilities.

• User education and training - Users must be adequately trained to identify phishing and social engineering attempts. They also need to understand and implement a company’s strong password policy. Employees should also realize the ramifications of sharing credentials and the potential it poses for data leaks.

• Backup data regularly - All data, especially high-risk information, should be backed up regularly to provide the necessary resources to recover in the event of a ransomware attack or disaster.

• Implement a modern DLP solution - A DLP solution automates the enforcement of data handling policies and can be instrumental in eliminating data leaks. Modern DLP tools handle the classification of data resources and protect them.

What is a DLP solution?

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.

What are the most important features of a DLP solution?

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.

• Dynamic data classification - The incredible volume and speed with which data is generated and consumed make it impracticable to attempt pre-classification. Modern tools perform dynamic classification of data on-the-fly, eliminating pre-classification and achieving more effective data loss prevention.

• Fast deployment with immediate visibility - A DLP solution that is fast to implement and easy to use offers out-of-the-box functionality so you can start understanding and protecting your data immediately. For example, pre-built data handling policies that are easily customizable help you get your DLP up and running while still providing the flexibility to update your policies as needed.  

• Machine Learning on the endpoint (MLn)™ -  Leveraging machine learning capabilities enables modern DLP solutions to baseline user activity to alert on deviations and detect abnormal behavior with ease and accuracy.

• Operating system compatibility - Operating system compatibility is essential for a DLP solution. Modern DLP tools address the need to protect endpoints that may be running Windows, Linux, or macOS operating systems. Effective DLP requires a consistent approach where all systems can be covered by the same tool.

• Human-readable and correlated reporting - A DLP solution should help address the skills gap by giving your SOC the context to investigate, assess, and respond readily without significant overhead, both at the user and group level.

• Incident-based user education - A modern DLP tool educates users regarding violations of the data handling policy. Through this education, overall data security is enhanced, and users better understand their role in protecting enterprise information.

• Behavior/user identity separation - By focusing on behavior, rather than user identities, modern DLP solutions maintain employee privacy and trust. 

  Business/personal differentiation - An innovative DLP tool should distinguish between personal and business use and profiles to apply different policies and actions for personal vs. business use.

• Minimal impact on system performance - The last thing you want is for your DLP tool to negatively impact system performance and user productivity. Look for a tool that employs lightweight agents to enforce the data handling policy with minimal effects on system operations.

Conclusion