Home Data Engineering Data Education The Top 7 Best Practices for a Successful DLP Implementation

The Top 7 Best Practices for a Successful DLP Implementation

 

Data loss prevention (DLP) software helps protect organizations against the loss, breach, or misuse of sensitive data. In April 2019 alone, 1.34 billion records were breached, which shows how common data breaches are.

DLP tools are part of a wider approach that encompasses tools, policies, and processes to protect important information. DLP solutions work by classifying sensitive data, monitoring data at rest, in motion, and in use, and enforcing remediation based on policy violations.

Read on to find out why DLP tools are important and seven best practices for implementing DLP at your organization.

Why Use DLP Software?

The main reasons for using DLP software are to:

Help achieve compliance with industry-specific regulations protecting sensitive information, including GDPR, HIPPA, and PCI DSS
Protect company secrets, intellectual property, and other intangible assets belonging to your organization
Secure data residing on cloud systems
Improve visibility over data, including where the data exists, who is using it, and what reasons is it being used for
Boost data security in organizations that have work from home or bring your own device options for employees.

The data loss prevention market is forecasted to grow by 23.59 percent from 2019 to 2014. Driving this growth are increased incidence of serious data breaches worldwide the extension of internal IT networks to the cloud, and more stringent compliance mandates that continue to evolve.

DLP Implementation Tips and Best Practices

Implementing DLP software is a big undertaking and a significant investment for any organization. Using a DLP solution calls for a clearly defined plan to improve the chances of a successful implementation. Here are some DLP best practices around which you can build a working strategy.

1. Identify and Classify Sensitive Data

Identify the data that would cause the biggest issue if it was exposed in a data leak or loss incident. The most sensitive data at organizations varies according to the industry they operate in. In healthcare, for example, the most important data to secure is Protected Health Information (PHI), while in a manufacturing organization it’s likely to be intellectual property.

Not only is it important to identify sensitive information; you also need to track how this sensitive data flows between different systems. A DLP tool is able to track the path and location of all sensitive information, however, you need to first classify the data to track its movement. You can classify by context, such as the type of data or the data store in which the information resides.

2. Perform Due Diligence on Vendors

Due diligence is imperative in choosing the right DLP tool, and it’s a good idea to create an evaluation framework of important questions you need to ask about potential vendor tools. Some of the questions you should ask include:

  • Can the vendor track data movement based on policies, events, and user actions?
  • What operating systems does the vendor support?
  • Is the solution a managed service or will the vendor provide traditional IT support?
  • Can the proposed tool support compliance with any regulations your organization is bound by?

Asking these types of questions when evaluating all vendors makes it more likely you’ll end up with the most suitable DLP software for your organization.   

3. Define Roles and Responsibilities

You must have a clear definition of roles and responsibilities for using and maintaining your DLP software. It’s prudent to segregate roles based on who creates the DLP policy and who implements the policy rules in your chosen system. For example, the security team can create rules based on data security needs, but your IT support system can implement the policy in your DLP tool.

Clear roles and responsibilities are also crucial for promptly responding to any potential data loss or leak incidents that the DLP system flags.

4. Go for a Simple Win First

With something as complex as DLP, it is unwise to go “all-in” from the outset. Begin by trying to secure a subset of your most sensitive data to get a simple win with the tool before extending to more data. This pilot project can teach valuable lessons for extending the DLP solution to a full-scale deployment while also growing confidence in the system.

In the initial pilot phase, perhaps start by monitoring the data only before moving on to blocking user actions or auto-encrypting data as it moves across systems.

5. Test Your Policies Before Deployment

DLP solutions generate alerts using policy-based rules. These alerts are then escalated to support teams or incident response teams. It’s important to test policies before going live because high volumes of false positives can frustrate support teams and disrupt normal business processes.

6. Know Your Tool’s Limitations

Before using a DLP tool, it’s critical to know the limitations of this technology. DLP tools provide more visibility, control, and protection of sensitive data, but they cannot analyze encrypted data without the keys to decrypt the files. Another limitation is that organizations with a lot of sensitive data contained in rich media such as graphics files will struggle to get much use from DLP tools because they are unable to parse and classify this type of content.

7. Define Metrics to Prove Success

With any large-scale business investment, there is inevitably pressure from stakeholders at the executive level to prove the value that the investment provides. It is vital to define DLP metrics and success criteria. After defining the key performance indicators, you need to ensure they are regularly collected and reported on to relevant stakeholders.

Useful metrics include the number of false positives (ideally close to zero), detection accuracy, number of data loss incidents since implementation, etc.

Closing Thoughts

A DLP strategy and tool are both critical in the modern cybersecurity landscape. Data breaches happen with worrying regularity, and a full-suite DLP tool gives you a better chance of preventing accidental data exposure and third-party/insider attacks.

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