What Is a Data Loss Prevention Policy?
Data loss can trigger a cascade of adverse consequences for businesses, resulting in financial and reputational damage. Today’s organizations use data loss prevention (DLP) policies to govern access to their data and safeguard its confidentiality and integrity. In this guide, we’ll explore the primary causes of data loss, recommend best practices for building a data loss prevention policy, and explain how a data loss prevention solution works in tandem with a DLP policy to strengthen an organization’s security posture.
What Is a Data Loss Prevention Policy?
A data loss prevention policy consists of the guidelines and procedures specific to an organization that are necessary to prevent data leakage or unauthorized access. A DLP policy consists of several components, including:
- The various types of data that need to be protected
- Procedures for accessing and sharing each type of data
- The data security technologies and methodologies that will be employed, such as data access controls, encryption, and data monitoring
- Measures to address compliance
- A strategy for responding to a data security event, prescribing next steps to accelerate mitigation, and executing recovery efforts
Specialized data loss prevention solutions are used to enforce the directives contained in the data loss prevention policy, providing a centralized approach to data classification, monitoring, and access control enforcement.
Primary Causes of Data Loss
Even the most well-prepared organization can fall victim to a data breach or incident that causes data loss. Here are the three primary ways this may happen:
Disgruntled employees can use privileged access to steal data. Motives range from settling a score with a former employer, expressing dissatisfaction with what they perceive as unfair workplace policies or management practices, or simply benefiting financially from selling confidential data such as trade secrets. Using their login credentials, malicious insiders can delete, corrupt, or exfiltrate sensitive company data.
Poor decisions from well-intentioned employees are one of the most common causes of data loss. Actions that endanger security include clicking on a link in a phishing email, accidentally exposing login credentials, or sharing sensitive data with an outside source, such as a vendor or partner organization, without considering the nature of the data.
External threats come from unauthorized entities that gain access to an organization’s network, often using compromised credentials belonging to authorized users. These intruders can often go undetected for weeks or months as they penetrate deeper into the network seeking increasingly elevated levels of privileged access.
Best Practices for Building a DLP Policy
A data loss prevention policy is essential to any business’s cybersecurity strategy. But tailoring the policy to meet an organization’s unique needs can be challenging since it requires a comprehensive understanding of the entire business data architecture and all potential risks. Here are seven best practices for building an effective, comprehensive DLP policy:
1. Identify Sensitive Data, Prioritizing by Order of Importance
Data loss prevention begins by identifying various types of sensitive data owned by the organization. Many companies work with multiple types of sensitive data, including social security numbers (SSNs), consumer credit card information, trade secrets, financial data, protected health information (PHI), personally identifiable information (PII), trade secrets, and intellectual property (IP). Many of these data types fall under one or more government and industry data regulations, each with its own set of requirements. A data inventory helps businesses understand which data should be prioritized for protection and how it must be secured to meet relevant regulatory compliance standards.
2. Locate Where the Data Is Stored
With data spread across a complex grid of cloud and on-premises networks, systems, and applications, understanding where sensitive data exists can be difficult and time-consuming. Although highly distributed systems offer many benefits, they drastically increase the complexity of locating sensitive data stores. To address this challenge, businesses are using data security solutions that can aid in data discovery and auto-detect sensitive data.
3. Classify and Tag Data Sources by Data Type (PII, PCI, PHI)
Once data has been located, sensitive data should be classified and tagged across cloud data platforms and other data stores. This step enables organizations to determine the criticality and sensitivity of the data, and identify appropriate protection and retention controls for each type.
4. Determine User Roles and Levels of Data Access
The need for access to sensitive data varies. For example, a data engineer working on a machine learning project will require very different roles and permissions than someone in the fraud prevention unit of the finance department. And synthetic users such as APIs and service-level accounts all have varying data access requirements. Users should only be given access to the specific data they need. Carefully defining user roles and access levels helps ensure only those with a need to know can access sensitive data.
5. Track Data Movements
Monitoring how data is used in an organization helps teams spot weaknesses in the organization’s data security posture. Tracking data at rest, in transit, and in use provides valuable insights into when and where the data is most vulnerable, and helps identify risky behaviors such as downloading data onto a portable storage device or transmitting it in an email attachment. Businesses should implement monitoring across the organization’s entire data ecosystem, tracking requests and access to data, policy changes, and how data is being used.
6. Predefine the Remedial Actions for Responding to a Security Event
What happens next is crucial to mitigating the fallout when a security event occurs. Determining what actions must be taken and by whom ensures a consistent approach to policy violations with automated actions such as blocking the operation and investigating the incident to prevent deeper access. Data security tools can assist with incident response by automating actions to address numerous types of data security violations, suspicious patterns of usage, and risky behavior.
7. Determine How Data Security Information will be Archived
A comprehensive DLP policy should include rules for archiving data. This information may include a data audit trail and information about IT security incidents. Archived data can be invaluable for threat hunting, incident investigations, and post-incident remediation.
How Do Data Loss Prevention Solutions Work?
Data security solutions use technology to enforce the data protection protocols in a DLP policy. These tools are essential in today’s complex hybrid and multi-cloud environments, helping monitor, detect, and prevent data leakage across the company’s entire data footprint. There are several vital capabilities that a data security solution may offer to help prevent data loss:
Data Discovery and Classification
Using machine learning (ML) and other advanced technologies, data security platforms can scan enormous information stacks, automatically detecting sensitive data and generating standard tagging across multiple platforms. They reduce manual, error-prone processes and provide universal data access control and visibility into sensitive data.
Data Exfiltration Detection
Data security platforms protect endpoints such as laptops, desktops, and mobile devices from data leaks by monitoring and controlling data transfer and access. Advanced solutions track data within the organization’s cloud and on-premises networks, as well as on remote devices such as laptops, smartphones, and tablets. When it detects sensitive data in danger of exfiltration, the system can automatically send a security alert, change permissions for the data, and even block the data transfer from happening.
DLP platforms enforce policies to prevent unauthorized data access or transmission. These tools can monitor real-time data streams and immediately restrict suspicious activity. Examples of these actions may include blocking access to certain websites or preventing data sharing through email or cloud services.
Continuous Monitoring and Analysis
Data security platforms also provide advanced reporting and analytics capabilities that allow security teams to detect high-risk activity or behavior. This information enables security teams to monitor and evaluate data protection policies and identify areas for further improvement.
Data Loss Prevention Policy: The Blueprint for Protecting Sensitive Data From Compromise
As data security becomes ever more critical to business success, the importance of creating and implementing a comprehensive data loss prevention policy has never been greater. By pairing a DLP policy with a data security platform to assist in implementing it, organizations can improve security and unlock the full value of their sensitive data while shielding themselves from risk.
For an inside look at the state of data policy management, check out The Data Policy Management Report.