What is Data Loss Prevention (DLP)?
The definition of Data Loss Prevention encompasses a set of practices and tools designed to prevent data leakage (also known as data exfiltration) due to intentional and unintentional misuse. These practices and tools include encryption, detection, preventive measures, educational pop-ups (for unintentional movements), and even machine learning to assess user risk scores. Over time, DLP has evolved in the field of data protection and has become a key feature of data protection implementation.
For simplicity, we will use the acronym “DLP” throughout this guide to refer to all of these measures, unless otherwise noted.
The Need for Data Loss Prevention
Data loss is very damaging to business. It erodes trust in your brand and can lead to financial losses from lawsuits, fines for regulatory non-compliance, and exposure of intellectual property. Let’s take a closer look at the requirements that drive the need for DLP.
1. Compliance with government and industry regulations
Many industries, such as healthcare, government contractors, and financial institutions, are required by law to protect sensitive personal data. Some of the regulations include:
- HIPAA (U.S. Health Insurance Portability and Accountability Act)
- GDPR (European Union General Data Protection Regulation)
- PCI DSS (Payment Card Industry Data Security Standard)
- CCPA (California Consumer Privacy Act)
- PIPEDA (Canada’s Personal Information Protection and Electronic Documents Act)
Something that all these regulations have in common is the stipulation that confidential data must be stored in a secure location and out of the reach of unauthorized users. Companies must have DLP strategies and tools in place to prevent unintentional or malicious access to isolated data storage and the leakage of such data to the outside world.
2. Protection of confidential information
The concept of confidential information refers to any confidential data or knowledge about the organization and its structure and business operations, or about its customers, consumers, partners, or affiliates. Some examples of confidential information are:
- Internal project plans
- Confidential code
- Patent information
- Email communications
- Business documents
- Internal processes
Although some cybercriminals steal information from organizations and government agencies just to prove they can, most do so for the financial gain of selling or exposing that information. Today, many ransomware attackers not only encrypt the victim’s data and demand money to unlock it, but also leak some of the data and demand payment to keep it from being made public.
Data loss prevention software and strategies help keep intellectual property safe, not only from external attacks and leaks, but also from unintentional data leaks caused by your own employees. Careless sharing of confidential data and information through unsecure means and public cloud accounts can cause as much damage as malicious acts of information espionage.
How does data loss prevention work?
There are several DLP security methods, which are implemented through best practices and software tools. The best data loss prevention strategies include a variety of approaches to cover all possible vectors of breach.
The 5 types of data loss prevention
1. Data identification: This is the process organizations use to identify confidential information within their digital environment, whether it resides in email, cloud storage applications, collaboration applications, or elsewhere.
2. Data leak identification: This is an automated process to detect and identify misused data, whether it has been leaked externally or lost within an organization’s infrastructure.
3. Data-in-Motion DLP: When data is in transit between locations, DLP network security employs a variety of security measures to ensure that the data arrives untouched at its destination.
4. DLP for data at rest: This type of protection covers data that is not in transit and is typically stored in some type of database or file-sharing system. It uses various methods to ensure the secure storage of data on-premises and in the cloud, from endpoint protection to encryption to prevent unauthorized use of data.
5. DLP for data in use: Data being used by users within an organization must be protected from any potentially harmful interactions, such as alterations, screenshots, cutting/copying/pasting, printing, or transferring information. In this context, DLP aims to prevent unauthorized interactions or movements of data, as well as to note any suspicious patterns.
Data Loss Prevention Best Practices
1. Educate your employees
One of the most effective best practices for preventing data loss begins with training employees on all the dos and don’ts when handling your organization’s valuable data. Employee training on DLP should include secure practices for transferring, viewing, and storing data. For maximum effect, training should be actively supported by management and repeated at regular intervals to reinforce and update best practice behavior.
2. Establish policies for data handling
Data handling policies, which are a key component of DLP best practices, include:
- Where data can be stored
- How data should be transferred
- Who can view certain types of data
- What types of data are allowed to be stored
- And much more
Since these policies govern all other handling behaviors and assessments, they should be defined as early as possible. They should also be updated regularly to reflect changes in the organization, industry, and regulations. Once data handling policies are in place, you can move on to more technical solutions and best practices to ensure that data stays where it belongs.
3. Create a data classification system
The key to creating data loss prevention policies is to start with a data classification system. This taxonomy will provide a reference for discussing the severity and protection methods required for different types of data. The most common classifications include personally identifiable information (PII), financial information, public data, and intellectual property. But there are many more. A unique set of protection protocols can be established for each classification.
4. Control sensitive data
A good data protection strategy should offer the ability to control sensitive data. Data loss prevention software typically includes features for tracking all aspects of data usage and storage, including:
- User access
- Device access
- Application access
- Threat types
- Geographic locations
- Access times
- Data context
As part of the control process, DLP software sends alerts to relevant personnel when data is used, moved, deleted, or altered in an unauthorized manner.
5. Implement DLP software that has capacity for Shadow IT applications
It’s hard enough to protect the data used by your inventory of known applications. But you also need to consider data access from shadow IT applications. These are the growing number of software-as-a-service (SaaS) applications that employees subscribe to independently, without IT department approval and often without their knowledge.
Even if employees are well trained on DLP best practices, it is difficult for them to accurately assess the security of these cloud-based applications. In most SaaS models, the SaaS provider is responsible for the applications themselves, but users are responsible for the data used by the application. Users, who are focused on achieving business goals, are not in a position to protect themselves from attacks that may come through a compromised SaaS application. It is up to you to keep data leaks and misuse at bay. That’s why you need a DLP software solution that can recognize shadow IT applications and prevent users from accessing data or moving data to these applications until you can authorize and integrate them into your secure IT operations.
6. Establish different levels of authorization and access
This best practice goes hand in hand with data classification, as combining these two practices will allow you to grant access to data only to those who are authorized to use that information. Your DLP software should also incorporate certain zero-trust data protection policies that do not systematically trust any user, while constantly verifying identities and authorization.
7. Adopt complementary tools to DLP
DLP does not exist in a vacuum. The entire concept of DLP is based on an ecosystem of tools that work together to provide strategic information, action plans, and active protection for your data. These tools include secure web gateways, cloud access security brokers, email protection, and zero trust infrastructures.
I hope this is helpful.
