DLP tools protect data At Rest (on endpoints, servers, and file shares) and In Use. To ensure effectiveness, teams should review configurations and test new features regularly. The most successful programs recognize DLP as a continual process rather than a one-time implementation. To accomplish this, organizations should begin by identifying success metrics and sharing reporting with leaders.
Identifying Sensitive Data
With the right tools and processes, a DLP solution can help you determine where your sensitive data is going and who might be sending it. For example, by monitoring the digital identities of employees, vendors, and contractors with role-based access control, you can ensure that only the people who need to see that data have clearance. This goes hand-in-hand with data classification, which helps you categorize data based on its value and risk. DLP can identify sensitive information using several methods, including regular expression pattern detection, which looks for patterns like 16-digit credit card numbers or nine-digit Social Security numbers near other keywords. It can also use dictionaries, taxonomies, and linguistic rules to classify data and look for specific terms or concepts in unstructured documents. However, it cannot track modern mobile communications, such as messages sent from personal devices. Once you have visibility into the Use and movement of your most valuable data, a DLP program can prevent unauthorized loss, theft, or release. It can also help you stay ahead of evolving cybersecurity threats, including those that evade traditional protection tools.
Blocking Unauthorized Access
DLP solutions must block unauthorized access to protect sensitive information by detecting when data is moving through the network. This can be accomplished in several ways. One method involves file checksum analysis. This technique compares a checksum of file data with the original to determine if it has changed. Another method is partial data matching. This technique uses a combination of dictionaries, taxonomies, and linguistic rules to identify concepts that indicate sensitive information in unstructured data. A third method involves statistical analysis and machine learning to detect more obscure sensitive information. Once sensitive data has been identified, a cybersecurity team can configure the DLP system to respond with different rules and controls. These may include defining confidentiality levels like private, confidential, top secret, or credit card information. Once a confidentiality marker is triggered, the DLP solution can prompt users to take faster action by sending them an alert or stopping the transmission of the information. This automated response can help prevent data loss and educate employees on being more secure. Since most data breaches occur due to unintentional behavior by employees, user education can reduce the risks of data leaks from internal threats. This is why many DLP solutions offer interactive prompting to notify users when their activity may be risky or violate company policy.
Educating Employees
A comprehensive DLP solution includes features to identify, classify and protect intellectual property. Using automated classification to improve data handling policies and controls, these tools can help your team comply with industry regulations and standards. This technology can also help your team limit the risks associated with remote work. Educating employees also plays an essential role in the success of a DLP program. After all, human error often causes data loss, and a good DLP policy can minimize the threat. Educating employees via classes, written reminders, and other initiatives can improve their ability to follow recommended DLP best practices. Penalties for violations can motivate employees to comply with your organization’s data security measures. Getting the leadership on board for your DLP program is an essential first step, and this can be accomplished by demonstrating how the solution can address various business unit pain points. For instance, if your CFO has concerns about the security talent shortage, explain how managed DLP services can act as remote extensions of your team.
Monitoring Activity
When the DLP system detects confidential data, it notifies a cybersecurity team. The team reviews the occurrence and configures security rules determining how to respond. Depending on the rule, this could include sending an alert notification, blocking the transmission of sensitive data, or revoking access privileges. DLP solutions monitor where and how data moves throughout the organization, enabling the DLP team to see and analyze data that would otherwise be hidden or invisible. This information helps prevent data leakage and reduces risk. As a result, the DLP team can better protect data from adversaries and comply with regulatory mandates. When you’re ready to implement a DLP solution, get leadership on board and have leaders from every department involved. They should be familiar with the tool and understand how it will affect their departments’ workflows. It’s also important to start small, use a project-based approach, or use a phased deployment strategy. This will help avoid overwhelming central business operations with too much DLP activity. It will also allow you to tweak DLP strategies and features as new capabilities emerge. It’s also important to regularly test and perform adversary emulation exercises to ensure the DLP tool is working as intended.
