5 Data Loss Prevention Best Practices

Data breaches receive much media scrutiny when they occur because sensitive data, often belonging to individuals, ends up in the hands of unauthorized parties. Organizations typically store different forms of sensitive data on their internal network, from Personally Identifiable Information (PII) to intellectual property. All organizations with sensitive data have the responsibility and duty to protect it.

Read on to find out about what data loss prevention (DLP) is. You will also get five best practices for successfully implementing a DLP plan.

What Is Data Loss Prevention (DLP)?

Data loss prevention (DLP) combines tools and processes to protect against both data loss and data leaks. A data leak is the transmission of sensitive information to an unauthorized party outside an organization’s IT network. A data loss incident occurs when you lose access to sensitive data, whether due to hardware failure, accidental deletion, or malware attacks.

Important use cases for implementing a DLP approach include protecting personal information, complying with industry regulations, protecting intellectual property and company secrets, and improving data visibility. DLP can address other pain points like insider threats and behavior analysis.

Some factors influencing the growth of DLP adoption (both strategies and software) are:

  • The need to evolve with changing industry regulations governing sensitive data that tend to become stricter over time
  • The continued incidents in which serious data breaches target even the largest enterprises like Equifax and Yahoo, eroding customer trust.
  • The change in IT infrastructure to move towards increased use of cloud computing services that blur the boundary between internal and external networks.
  • Digital transformation in industries like healthcare with increased movement from paper to electronic records, which necessitates defenses against data loss and leaks.

Best Practices for a Successful DLP Plan

1. Prioritize and Classify Data

Classifying data means applying tags that enable organizations to monitor and track data use with DLP tools. It’s advisable to use simple and explanatory category tags, such as “regulated data,” “credit card numbers,” “intellectual property.” The full list of categories will vary across businesses but categorization is a crucial starting point that sets the tone for the rest of the DLP implementation.

Prioritizing data means identifying and ordering data types based on the severity of impact a loss or leak incident would have. A manufacturer might have design files as a top priority to protect but there will be other types of sensitive data too. DLP implementation should begin with the most sensitive data.

2. Define Roles and Responsibilities

For data loss prevention to work, you need to clearly define roles and responsibilities, both to people responsible for implementing the plan and users of sensitive data. The principle of least privilege ensures users only get access to the data that is strictly necessary for doing their jobs.

Many DLP solutions come with pre-configured rules based on regulations, such as HIPAA or GDPR, however, the information security team should adapt this to their specific organization and create custom policies. Also, define who is responsible for responding to alerts and incidents. This step provides structure and clarity to the DLP implementation.

3. Understand the Three Data States

It’s crucial to understand when your different types of sensitive data are at most risk. The risk profile typically relates to the different states data can be in: at rest, in use, and in motion. An effective DLP implementation must adopt appropriate techniques to data that exists in each of these states.

Data at rest resides in databases, file systems, or cloud storage centers. Applying strong cryptographic algorithms to encrypt data at rest can protect it from loss or breach. You can also use policy-based rules to delete or alert you about sensitive data at rest if your storage DLP tool finds it in unauthorized servers or other storage locations.

Network DLP technology inspects network traffic and files moving across or outside the network and takes policy-based actions to protect sensitive content. For data in use at endpoint devices like laptops and workstations, endpoint DLP solutions can track user behavior and block certain actions like copying sensitive data to USB drives.

It’s important to note that some DLP tools focus on protecting data in one of the three risk states: storage, endpoint, and network, while others are all-in-one solutions.

4. Deploy Cautiously

Identify a subset of your most critical data and focus on securing it with your DLP plan. Use this pilot project as the basis for expanding your DLP plan to cover larger stores of sensitive data. A comprehensive initial rollout is likely to frustrate security teams and support staff and disrupt ongoing business processes.

Not only does a phased deployment help you grow in confidence, but it also protects first against the loss or leak of the most important business data.

5. Prove DLP Value

At the executive level, stakeholders want concrete proof that an initiative or technology is working for an organization, and DLP is no exception. Because successful DLP combines enterprise-grade tools, new policies, and a shift in approach, it is a time-consuming and expensive initiative.

Make sure you can provide key performance indicators (KPIs) and reports that clearly display the value of your DLP implementation, particularly when it comes to software. Look out for metrics like the number of data loss or breach incidents since implementing a DLP plan, data loss trends over time by severity, and how accurately your DLP solution detects true data loss incidents.

Conclusion

Data loss and breaches continue to rise because sensitive information is such a lucrative reward for cybercriminals. The average cost of a data breach in 2018 was $148 per record stolen and cost organizations an average of $3.86 million per breach.

Successful DLP is not just the use of high-tech tools and strategies — it is a movement that sees data protection as a central tenet of a modern digital organization’s culture.

This UrIoTNews article is syndicated fromDzone