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Updated: Jun 7, 2024   |   Fergal Glynn

6 steps to implementing a successful data loss prevention strategy

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6 steps to implementing a successful data loss prevention strategy

Data-driven companies need a data loss prevention (DLP) strategy to protect their valuable information. Enterprises must guard against data being compromised, lost, or misused deliberately or accidentally. The same level of damage can be caused by a data breach initiated by a cyberattack or one triggered by an employee’s accidental disclosure of intellectual property via unencrypted email.

Implementing a successful DLP strategy requires a methodical approach that addresses the needs of the business and the type of data it gathers, stores, and processes. The following steps illustrate the best practices that should be part of a company’s DLP strategy.

Create a data handling policy

A company’s data handling policy must align with business requirements and reflect its information resources. If a company has a typical mix of data assets, it may be able to use a data handling policy template that can be tailored to the organization’s specific needs.

This policy defines classes of data that require different types of handling to protect the organization. Typically, at least three kinds of data are classified based on the information’s importance and the damage its misuse or loss can cause:

  • High-risk data includes sensitive data and intellectual property that can cause extensive damage if lost or compromised. If the company operates in a regulated industry, the data handling policy has to address issues such as compliance with security and privacy standards and regulations such as HIPAA and GDPR. Information subject to regulatory standards will almost always fall into the high-risk category.

  • Medium-risk data can cause less extensive damage to an organization. It may include operational guides that might interest competitors but will not negatively affect the business.

  • Low-risk data does not need additional protection and can be freely distributed to the public. Informative guides and user manuals that help customers use a company’s products are examples of low-risk data.

The policy will define how differently classified data elements are handled within the company. For example, it may specify that all high and medium-risk data be encrypted before transmission over public networks. It may restrict the use of sensitive information to a small group of authorized users. The data handling policy is the foundation upon which a DLP strategy is built, so it’s worth taking the time to get it right.  

Classify all data resources

Once a data handling policy has been created, a company’s information resources must be classified. This includes discovering data assets across the entire computing environment so they can be classified. 

Legacy DLP solutions required data discovery and classification to be performed manually. However, modern DLP tools like Next DLP’s Reveal automate this process by dynamically discovering and classifying data for enhanced accuracy, eliminating the need for additional classification tools. 

Identify data vulnerabilities

Companies must identify situations or activities that present data vulnerabilities. This is often an ongoing activity that evolves as a DLP strategy matures or business requirements change. The discovered vulnerabilities offer focus points for enforcing the data handling policy.

Examples of data vulnerabilities include:

  • Activities that demand the sharing of sensitive data via email

  • Transmitting high-risk data from remote endpoints

  • Storing sensitive data in public cloud storage

The next step of the DLP strategy addresses these vulnerabilities.

Enforce the data handling policy

The main purpose of a data loss prevention strategy is to enforce the company’s data handling policies. A reliable DLP solution should be capable of enforcing the policy through the infrastructure. It should be equally effective in handling newly created data, data residing in legacy systems, and data recently ingested into the environment.

Automated enforcement will perform activities to protect data before it is lost or misused.

Examples of the enforcement of a data handling policy include:

  • Automatically encrypting high-risk data at all times
  • Preventing unauthorized users from viewing or printing sensitive data
  • Classifying data effectively as it is incorporated into the environment

Monitor data movement

Continuous monitoring of data movement is critical for a successful DLP strategy. Every time data is exchanged, transferred, or accessed, it should be subject to the data handling policy. This includes all internal and external uses of data resources.

Provide ongoing education

Employees should be provided with ongoing education regarding the enterprise data handling policy and their role in protecting information resources. Ideally, a DLP solution offers real-time guidance when a DLP policy violation occurs so the individual understands how they need to modify their behavior in the future.

Deploying a modern DLP solution

Next DLP offers customers a human-centric DLP solution that discovers risks, remediates them by enforcing a data handling policy, and educates its users. Our Reveal product is a cloud-native solution that is easy to install and use. It provides immediate results with configurable data handling policies and machine learning, provides smart remediation and enforces the policy even when computers are disconnected from the network.

Reveal employs lightweight agents for Windows, Linux, and macOS computers that won’t impact performance or productivity. Real-time, incident-based training is also furnished to employees to help improve their understanding of the data handling policy.

Contact the Next team or book a demo to see how easy it is to get started with a successful data loss prevention strategy. 


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