Calendar Icon White
June 25, 2024
Clock Icon
 min read

Data Loss Prevention Process: A Comprehensive Guide

LinkedIn Logomark White
Data Loss Prevention Process: A Comprehensive Guide
Calendar Icon White
June 25, 2024
Clock Icon
 min read

Data Loss Prevention Process: A Comprehensive Guide



  • Implementing a robust data loss prevention (DLP) process is crucial for protecting sensitive information and avoiding the devastating consequences of data breaches. 
  • This guide outlines key steps for developing an effective DLP strategy, including data classification, risk assessment, policy establishment, deployment of controls, and continuous improvement. 
  • Leveraging modern DLP technology can automate data discovery and classification, ensuring comprehensive protection across networks, endpoints, and cloud environments.

Data loss can have devastating consequences for any organization. Sensitive information falling into the wrong hands can lead to reputational damage, loss of intellectual property, regulatory fines, and lawsuits. That's why implementing a robust data loss prevention (DLP) process is critical. This guide will walk you through the key steps involved in developing an effective DLP strategy.

Strac Data Loss Prevention Process: Customer Positive Review
Strac Data Loss Prevention Process: Customer Positive Review

Defining Data Classification Levels in the Data Loss Prevention Process

The first step is to classify your data based on sensitivity levels. This allows you to apply appropriate protections to your highest-risk data. Common classification levels include:

  • Public: Information that can be freely disseminated with minimal security controls. This may include marketing collateral, press releases, etc.
  • Internal: Information intended for internal use only. This could include organizational charts, internal memos, policies and procedures.
  • Confidential: Sensitive information that requires stronger access controls and encryption. This may include customer data, financial reports, product designs etc.
  • Regulated: Data governed by regulatory compliance standards like HIPAA, PCI DSS, and GDPR. Requires the highest levels of security and audit controls. Includes personal health records, credit card data, personal information etc.

Conducting Data Discovery and Classification in the DLP Process

Once you have defined classification levels, the next step is to discover and classify your organization's data assets. This involves:

  • Identifying data storage locations - on-premises servers, cloud platforms, endpoints etc.
  • Determining data types - customer info, financial data, intellectual property etc.
  • Scanning and indexing data to enable classification into defined levels.
  • Tagging or labeling classified data for ease of identification and control.

Modern DLP solutions can automate much of this process using machine learning algorithms. Manual classification may be required for legacy systems. Regular classification reviews are recommended as data landscapes evolve.

Perform Risk Assessment

With your data classified, you can now perform a risk assessment by analyzing:

  • What are the potential threats from insiders, hackers, malware etc?
  • What are the impacts of data loss or leakage? Financial, reputational, regulatory etc.
  • Where are the vulnerabilities in your infrastructure, policies and processes?
  • What controls need to be implemented to mitigate identified risks?

This analysis will highlight high-risk areas to focus your DLP efforts.

Establishing Policies and Controls in the Data Loss Prevention Process

Now you can establish DLP policies aligned to your data classification levels. These policies dictate the security controls applied to data such as:

  • Access restrictions to limit data visibility
  • Encryption protocols for data at rest and in transit
  • Usage authorizations to prevent unauthorized activities
  • Copying protections to block duplication to removable media
  • Sharing rules to prevent emailing or uploading sensitive data
  • DLP monitoring, alerting and response procedures

The policy should also define acceptable use guidelines for employees. Integrating the policy into security awareness training is key.

Deploy Data Monitoring and Controls

Strac Data Loss Prevention Process: Gmail Before Redaction

Strac Data Loss Prevention Process: Gmail After Redaction

With policies set, DLP controls can be deployed through:

  • Network DLP - monitors and controls data movement across network channels. This prevents data exfiltration and unauthorized sharing.
  • Endpoint DLP - controls endpoint activities like printing and transferring data to USB devices. Prevents data theft and loss.
  • Cloud DLP - secures data stored in SaaS apps and cloud platforms. Prevents external sharing of sensitive cloud data.
  • Email DLP - analyzes and restricts sensitive data shared over email. Helps prevent accidental data leaks.
  • DLP for Removable Media - blocks copying of restricted data to removable media like USB drives. Reduces data theft and loss risks.

These controls should cover all potential data leak channels based on your infrastructure and risk profile.

When implementing a data loss prevention process, it's crucial to consider solutions that offer comprehensive protection across various environments. Strac's DLP solution provides advanced features like AI-powered detection and inline redaction, which can significantly enhance your DLP process. 

Its ability to protect data across SaaS, cloud, and endpoint environments ensures a robust and adaptable data loss prevention strategy.

Enabling DLP Monitoring and Response in the Data Loss Prevention Process

Ongoing monitoring, alerting and response capabilities are critical for effective DLP. Key features include:

  • Logs of all DLP policy matches and data access attempts
  • Configurable alerts for immediate notification of incidents
  • Workflow automation to flag events for investigation
  • Incident response mechanisms to contain and remediate events
  • Reporting dashboards for visibility into DLP effectiveness

This enables rapid detection of and response to potential data breach incidents before they escalate.

Refine Through Feedback Loops

DLP strategies require continuous improvement in response to new threats and changing data landscapes. Key refinement activities include:

  • Tuning policies and controls to reduce false positives and improve detection accuracy.
  • Enhancing user awareness through updated training programs.
  • Expanding protections to cover additional data repositories and leak channels.
  • Updating classifications to reflect creation of new sensitive data types.
  • Conducting audits and risk assessments to identify control gaps.
  • Monitoring industry trends to benchmark against best practices.

This ongoing feedback loop is essential for long-term DLP success.

Leverage DLP Technology for Data Discovery and Classification

Technology plays a key role in automating the data discovery and classification process. DLP solutions leverage various techniques:

  • Content inspection - scans and indexes data content across structured and unstructured data sources on premises and in the cloud. This builds a central data catalogue.
  • Pattern matching - uses regex, keywords, dictionaries and other content-based patterns to accurately identify sensitive data types like credit cards, social security numbers etc.
  • Machine learning and statistical analysis - learns normal behavior and detects anomalies to identify potential data risks. Also classifies data by analyzing contextual cues.
  • Metadata analysis - uses embedded metadata like tags, labels and headers to classify data without having to scan contents.

The top DLP tools on the market leverage combinations of these techniques for comprehensive coverage across the data estate. This eliminates blind spots and ensures sensitive data is accurately identified for classification.

Establish Data Retention and Destruction Policies

A core component of the DLP strategy should be establishing data retention and destruction policies aligned with classification levels. This involves:

  • Defining mandatory retention periods to meet regulatory and business requirements.
  • Setting recommended retention periods based on actual business needs.
  • Establishing destruction procedures when retention periods expire - whether it be physical destruction for paper records or secure deletion methods for electronic data.
  • Implementing controls around data retention - such as legal holds for data subject to subpoena or automated deletion after expiry.
  • Ensuring redundant, obsolete and trivial (ROT) data is disposed of to reduce security risks.
  • Providing clear audit trails to demonstrate compliance with disposal procedures.

Stringent retention and destruction policies prevent outdated data from accumulating and posing unnecessary security, privacy and compliance risks.

How Strac Can Help

Strac offers a comprehensive data loss prevention solution that can significantly enhance your DLP process. As a SaaS/Cloud DLP and Endpoint DLP solution, Strac provides modern features designed to streamline and automate your data protection process across your entire digital ecosystem.

Strac's built-in and custom detectors support all sensitive data elements for PCI, HIPAA, GDPR, and any confidential data, allowing you to implement a robust DLP process effortlessly. Uniquely, Strac offers detection and redaction capabilities for images and deep content inspection for various document formats. Explore Strac's full catalog of sensitive data elements to see how it can bolster your DLP process.

For organizations concerned about compliance, Strac DLP helps achieve standards for PCISOC 2HIPAAISO-27001CCPA, GDPR, and NIST frameworks, automating many compliance-related processes. With easy integration, customers can implement Strac and see live scanning and redaction on their SaaS apps in under 10 minutes, quickly operationalizing your DLP process.

Strac's machine learning models ensure accurate detection and redaction of sensitive PII, PHI, PCI, and confidential data, minimizing false positives and negatives and streamlining your DLP process. The solution offers extensive SaaS integrations, including AI integration with LLM APIs and AI websites like ChatGPT, Google Bard, and Microsoft Copilot.

For comprehensive protection, Strac provides Endpoint DLP that works across SaaS, Cloud, and Endpoint environments. Developers can leverage Strac's API support for custom implementations, while inline redaction capabilities ensure sensitive text is masked or blurred within attachments, further automating your DLP process.

Strac's customizable configurations and out-of-the-box compliance templates allow for flexible, tailored data protection measures that align with your specific DLP process. Don't just take our word for it – check out our satisfied customers' reviews on G2 to see how Strac has improved their DLP process.

In Summary

An effective DLP process involves:

  • Classifying data based on sensitivity levels
  • Discovering and tagging sensitive information
  • Performing risk analysis to prioritize protection
  • Establishing access control and usage policies
  • Deploying controls across network, endpoint and cloud environments
  • Monitoring policy adherence and responding to incidents
  • Continuously improving the program over time

With the right DLP strategy, organizations can drastically reduce their risk of damaging data breaches. A "set it and forget it" approach will fail against today's sophisticated threats. DLP must evolve alongside the business to provide reliable data protection over the long term.

Ready to enhance your data loss prevention process with a cutting-edge solution? Schedule a demo with Strac to see how our comprehensive DLP solution can streamline and strengthen your data protection strategy. Join the ranks of satisfied customers who trust Strac for their most critical data security needs.

Latest articles

Browse all