Laptop with creative glowing padlock interface

What is Data Loss Prevention?

Lesson From Mysteries

No one notices the driver, restaurant server, or mail carrier. That’s one of the lessons from mystery shows. Those characters aren’t suspicious – they’re among the most ordinary. They perform everyday necessary tasks reliably and without fanfare. Their attention to detail keeps the world moving. And those aspects are also part of what makes other people not notice those show characters when something goes missing (or worse!). (NOTE: This is about show characters – this is not a reflection on any real person).

Data Loss Prevention (DLP, aka Data Leak Prevention) involves the safekeeping of all kinds of data (e.g., personal health information (ePHI), confidential, and proprietary) from all manner of threats. One category of data thieves that gets the grandstand in the media is Advanced Persistent Threats (APTs), which usually involves nation-state military campaigns. But a couple threats that can go unnoticed are employees and contractors.

DLP isn’t about watching out for external criminals, nor is it about distrusting employees and contractors. DLP is all about protecting data, and that involves a 360-view of data risk.

There are definitely criminals at work, but data is not just lost through thievery. It’s also lost through mistakes (e.g., sending emails to the wrong people), negligence (e.g., forgetting to backup data), and oversight (e.g., developer forgot to secure an S3 bucket and a security researcher found it).

Under Lock and Key

Data Loss Prevention can be compared to a strong lock and key system. Just as with securing physical assets with locks and keys to prevent unauthorized access, DLP safeguards digital assets by implementing measures to detect, prevent, and mitigate data loss.

Data Discovery and Classification

Data Discovery and Classification are crucial components of Data Loss Prevention (DLP) strategies. Let’s look closer:

Data Discovery

Data discovery is the process of locating and identifying sensitive or valuable data within an organization’s digital environment. The goal? To create a comprehensive inventory of data assets, including structured and unstructured data, across various storage systems, databases, servers, and endpoints. (Inventory is the #1 step for any security solution – you can’t protect what you don’t know about. See the Center for Internet Security (CIS) Controls 1 & 2 for more information).

Data Classification

Data classification is the categorization of data based on its sensitivity, value, and regulatory requirements. It involves assigning labels or tags to data assets to indicate their level of confidentiality, integrity, and availability. Classification labels typically include designations like “public,” “internal,” or “confidential.” Or specific categories such as “personal identifiable information” (PII) or “intellectual property.”

By classifying data, organizations can prioritize their data protection efforts, allocate appropriate security controls, and define access privileges based on the sensitivity of the information. It also helps in implementing data retention and disposal policies.

Data Encryption

Data encryption is fundamental. It ensures that even if unauthorized individuals gain access to the data, they cannot decipher its contents without the encryption key. Here’s a closer look:

How Data Encryption Works

Data encryption involves the transformation of plaintext (original data) into ciphertext (encrypted data) using cryptographic algorithms and keys. The encryption process relies on complex mathematical computations that scramble the data in a way that it becomes unintelligible to anyone without the decryption key.

For details about the two types of encryption, here’s a quick read.

Encryption provides information confidentiality, compliance with regulatory standards, data integrity, secure data storage and communication, and keeps it from being viewed even if it’s stolen.

User Behavior Monitoring

User Behavior Monitoring (UBM) is a critical component of DLP strategies, as it helps detect and mitigate insider threats, unauthorized activities, and suspicious behavior that could lead to data leakage. Some common ways to deploy User Behavior Monitoring include:

  • Endpoint Monitoring: installing monitoring agents or software on individual user devices, such as desktops, laptops, or mobile devices to track and record user activities, including file access, application usage, web browsing, and data transfers.
  • Network Traffic Monitoring: capturing and analyzing data packets flowing across the network infrastructure to observe user behavior, track data transfers, and identify anomalous patterns.
  • User Activity Logging: Logging needs to allow “for automatic logging and intervention any time a user takes prohibited actions (like downloading or emailing)maintaining detailed logs of user actions, including system logins, file access, data modifications, and application usage. These can be analyzed to identify patterns, anomalies, or deviations from normal behavior.
  • Data Access Controls and Auditing:
    • Granular data access controls and auditing mechanisms help monitor and track user interactions with sensitive data. Access controls should enforce the principle of least privilege, ensuring that users have access only to the data necessary for their roles.
    • Auditing capabilities track user actions related to data access, modification, or deletion, enabling organizations to identify and investigate any suspicious or unauthorized activities.

Challenges In Implementing a DLP Strategy

Implementing a DLP strategy can be complex and challenging due to various factors. Here are a couple common challenges organizations may face:

  • Balancing Security and Productivity: DLP strategies aim to prevent data leakage, but they must be implemented in a way that balances security with employee productivity.
  • Ongoing Maintenance and Updates: DLP strategies require regular maintenance, updates, and continuous monitoring to stay effective. Technology advancements, new threat vectors, and evolving data protection regulations necessitate ongoing investment and efforts.

Questions, Questions

When evaluating and selecting Data Loss Prevention (DLP) tools for your organization, here are some key considerations.

What are specific data protection needs of the organization?
  • Types of sensitive data  – based on corporate risk assessment and goals – you need to protect (e.g., personally identifiable information, financial data, intellectual property).
  • Industry-specific compliance regulations that apply to your organization (e.g., GDPR, HIPAA, PCI-DSS).
What are the Deployment Options?
  • On-premises, cloud-based, or hybrid deployment model?
  • Scalability and flexibility of the tool to accommodate your organization’s growth and changing needs.
  • Can the tool seamlessly integrate with your organization’s existing security tools and technologies?
  • Assess the interoperability and compatibility with your organization’s network architecture and security ecosystem.
What Reporting and Compliance Features Does the DLP Tool Offer?
  • Evaluate the reporting capabilities of the tool (e.g., predefined reports and the ability to generate compliance reports for audits and regulatory requirements).
  • Does the tool provide robust logging and auditing features to track and record user activities, policy violations, and incident details?


From criminals to human error, threats to data lurk around every corner. Don’t let your valuable information slip through the cracks. Data – keep it secret, keep it safe.


Cyber Security Support Analyst with Passageways and Writer at Bora