Data Loss Prevention (DLP): Strategies to Implement and Myths to Avoid

Data Loss Prevention (DLP): Strategies to Implement and Myths to Avoid

A robust DLP strategy enables businesses to review and update the data storage and retention policies to maintain regulatory compliance and ensure the safety of sensitive information.

Deployment of a DLP program is a progressive process. A successful strategy initiation provides businesses with significant guidelines and helps them choose the right DLP solution. Here is a guide for DLP strategy and a few myths that hinder its deployment.

DLP Deployment Strategies

A powerful DLP software detects, monitors, and restricts sensitive data from exiting the organizational network. It allows users to flag questionable and inconsistent content and prompt them to examine or block the data manually. Here are a few DLP deployment strategies businesses must employ.

  • Data Prioritization and Classification

Every available data is not always critical. Hence, businesses must determine the data required for an efficient DLP strategy around it. Data classification is a challenging task with vast data generation. A quick and scalable approach is classifying the data by context per source applications, data storage, and the user that created the data. Regular data classification allows businesses to track their utilization.

More importantly, adequate “content inspection” helps companies to examine data to identify irregular expressions. This procedure applies when the data obtained has pre-configured rules for PCI or PII.
  • Acknowledge When the Data is at Risk

Detection risks in data vary with the data types. Encryption and network-based security controls offer security when data rests within the firewall. But, data distributed to user devices or shared with partners, customers, and the supply chain pose multiple other threats. Data is at critical risk due to the utilization of endpoints. A robust DLP program must be strategized around data mobility and situations when the data might be at risk.

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  • Data Movement Monitoring

Understanding the data usage and identifying the current behavior that causes risks in data is very important. According to a recent report by Zscaler, “2022 ThreatLabz Data Loss Report”, organizations see about 10,000 data loss policy violations daily. This allows organizations to develop appropriate policies to mitigate data loss risks while allowing adequate data usage. Hence, data movement controls the action and prevents data loss. Furthermore, businesses must monitor complete data movement to procure visibility into what is happening to confidential information and to determine the scope of issues that a DLP program must address.

  • Develop and Communicate Controls

Metrics generated by data monitoring will provide insights into how that data was put at risk. Businesses must understand the reason for the data risk and establish control for reducing data risks. Data usage controls may seem simple at the beginning of a DLP deployment. Hence businesses must target the common risky threats. Simultaneously, companies must establish more granular, fine-tuned controls to prevent risks as the DLP program matures.

  • Adequate Employee Training and Roll Out

After understanding the circumstances of displaced data, adequate employee training is necessary to mitigate accidental data losses within the organization. Employees sometimes fail to recognize their actions, which leads to data loss. Hence, businesses must teach regular self-mitigation strategies to prevent such losses. Modern DLP solutions offer users prompt functions that readily inform employees of data use that might violate organizational policies or accelerate risks.

At the same time, gaining control of the most critical data is a crucial step in DLP—organizations with expanded or extended data identification and classification to station fine-tuned data controls.

DLP Myths to Avoid

Organizations still hesitate to adopt DLP programs even though it has gained immense visibility among security and compliance teams. The hesitations are a result of a misunderstanding of DLP technology. Here are a few myths hindering the development of an effective DLP strategy.

  • Needs Enterprise-wide Effort

A common misconception about DLP is that it requires an enterprise-wide approach to initiate while it needs a thorough data analysis. DLP program supports comprehensive coverage time and allows businesses to aim and start small for a successful deployment. To ensure robust DLP deployment, companies must establish an easy-to-identify and sensitive category as these have a static application and a user group that needs the right to access. CISOs must discuss the requirements and ensure adequate buy-in. Furthermore, a business must add another data category once the pilot process works seamlessly.

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  • Networks will Suffocate

Network traffic content examination allows businesses to identify confidential data. However, it causes latencies, while repetitive content inspection does not elevate the data loss prevention methods. DLP does not need a review of each data packet traveling on the network. Fortunately, data can be classified since it can be created or amended on the endpoints. A significant classification tag is added to the data once organized. Intelligent endpoint agents can access these tags to station utilization rules as per user type, request action, and other contextual aspects. This offers businesses enhanced visibility and control without network disruptions.

  • Only Works Within Organizational Network

DLP is simple when correlated with the devices inside the network. However, it is believed that it is ineffective when deployed outside the networks or virtual environments. Interestingly, data-centric DLP is effective everywhere since the protection is not applied to the device or network but to the data itself. DLP at a data level actively prevents sensitive data from exiting the network and restricts the transfer of non-encrypted data.

  • Affects Productivity

Permissions and ineffective data classification methods infested traditional DLP solutions. Modern DLP does not affect legitimate users when applied to the data level. At the same time, endpoint agents automatically classify data and transparently enforces policies. This data loss prevention process effectively blocks unauthorized data and warns and prompts the users about questionable behavior. This capability strengthens the organization’s security policies and offers timely guidance.

As per a recent report by Zscaler, “2022 ThreatLabz Data Loss Report,”45% of the sensitive internal data is shared within the company, while risky external shares account for 55%. Of the external shares, 36% of shared files are with open public links. These statistics indicate risks that allow the threat actor to access the link actively.

A robust DLP strategy ensures that the user does not send critical information outside the corporate network. DLP tools can also filter data streams on the web, protect data in motion, and monitor and control endpoint activities.

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