In today’s data-driven economy, it is essential to protect an organization’s valuable information and intellectual property. Effectively accomplishing this objective requires a comprehensive approach to cybersecurity that involves multiple tools working together. Implementing true data security requires measures in addition to tools like firewalls and antivirus software that attempt to keep threat actors out of an infrastructure.
The way data is used throughout an organization also has to be addressed. Data loss prevention (DLP) focuses on ensuring a company’s data resources are protected against loss, unintentional disclosure, and inappropriate usage. This includes guarding against and preventing accidental or deliberate misuse of data assets by employees and contractors. However, companies often don’t know what to look for when they’re considering implementing a data loss prevention solution. Choosing the wrong solution could leave your business’s sensitive data vulnerable to attack, and data breaches are incredibly costly.
Companies interested in protecting their digital assets should implement a solution that provides the features necessary to secure data resources, while not negatively impacting an organization’s operational efficiency. In this article, we’ll review some important considerations when selecting a DLP solution to help you select the right solution to adequately protect your company’s sensitive data.
Factors to Consider When Evaluating a DLP Solution
Not all DLP solutions provide the necessary features and functionality to effectively protect a company’s valuable data. The following factors and characteristics should be considered when selecting a DLP solution.
Data Visibility, Discovery, and Classification
A DLP solution needs to provide visibility into an organization’s data resources, which can then be classified and handled appropriately. The effective classification of data resources is the foundation of DLP, and the selected solution should provide comprehensive context-based, content-based, and user-driven data classification.
Visibility into all sensitive data resources should be provided by efficiently scanning all local, network, and cloud repositories when the solution is implemented. This discovery process furnishes companies with immediate value, as potential violations are identified and addressed appropriately.
Additional classification can then be performed dynamically whenever data is created, modified, or ingested into the environment. In this way, all information is classified correctly and can be used according to the organization’s data handling policies.
Data Handling Policy Templates and Customization Options
Organizations can obtain immediate results from a DLP tool that incorporates built-in policy templates for common types of sensitive data. These include personally identifying information (PII) and protected health information (PHI) safeguarded by HIPAA regulations.
Implementing a default data handling policy enables the tool to start protecting sensitive information beginning with its initial scan. It can perform standard actions such as encrypting sensitive data before transmission to promptly improve security throughout an organization.
The ability to customize policy templates is equally important for a DLP tool. Companies may have unique data handling requirements that necessitate customization in how the information is classified, along with the responses that are initiated when a policy violation is detected. The tool should also have the ability to be amended and updated, so it can continue to be effective if new regulatory standards are later introduced.
Flexible Controls
The selected DLP tool should provide an organization with the ability to implement flexible and fine-grained controls regarding the enforcement of its data handling policy. Generalized or default templates may not address the specific needs of a company. For example, companies should be able to easily limit activities such as file transfers based on location or size.
Analytics and Reporting
The selected tool should provide access to analytics and reporting to help identify recurring policy violations. This, in turn, may indicate a need for security awareness training or a potential insider threat. Insights provided by the tool may also point out inefficiencies in the handling policy that are negatively impacting business operations.
Cross-platform Support
Effective data loss prevention requires the monitoring and enforcement of data handling policies at all endpoints. Organizations, therefore, need to adopt a solution that supports the different types of endpoints in the environment, and a cross-platform tool that supports a variety of operating systems.
Delivery Method
The delivery method used to implement a DLP solution needs to be carefully evaluated. Companies may opt for an on-premises implementation that involves providing the infrastructure components and technical personnel necessary to support the tool. Going in this direction may require training and hardware purchases that strain a company’s IT budget.
Another option is to go with a cloud-delivered solution that eliminates the need for hardware and onsite support. This type of tool will typically be more cost-effective and can be implemented much more quickly than an on-premises solution. Cloud delivery also supports the growing remote workforce who need DLP functionality wherever they are located.
Fortra DLP Delivers Results
Fortra DLP provides a comprehensive solution that addresses the factors outlined above. Our cloud-based DLP as a service reduces time-to-value, lowers costs, and scales to meet your organization’s evolving business requirements.
Data is automatically discovered and classified, ensuring all information is handled according to your organization’s policies. Fortra DLP’s cross-platform solution supports Windows, macOS, and Linux systems and endpoints, while fine-grained controls let you define exactly how policy violations are managed.
Schedule a demo with Fortra to experience how our advanced DLP solution protects your most valuable data.
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