An organization’s data is its most valuable asset. Companies use their information resources for many reasons, including obtaining insights into customer sentiment, monitoring market trends, and maintaining a competitive edge. That’s why data loss prevention (DLP) is so crucial for businesses of all sizes. DLP works by:
- Understanding content and context of data usage
- Classifying data based on rules you create
- Enforcing data handling policies
- Analyzing possible data security threats
- Educating employees on acceptable use
- Issuing alerts when risky behavior occurs
- Educating employees on data loss prevention best practices
The incredible volume of data generated by today’s businesses through multiple channels, coupled with the ever-evolving threat landscape, makes protecting data challenging. Organizations can sometimes have difficulty identifying the location of their information and determining its sensitivity so it can be properly protected.
In this article:
What is data loss prevention?
Data loss prevention (DLP) is a comprehensive approach to protecting a company’s information from external and internal threats. DLP is a collection of processes and tools that work together to ensure that an organization’s sensitive data is not lost, misused, or accessed by unauthorized users.
A DLP solution classifies data in real-time and looks for violations of an organization’s data handling policies. The policy may be based on regulatory compliance with standards such as HIPAA or GDPR. If violations are discovered, DLP helps to remediate the issue by implementing protective actions (e.g., clearing the Windows clipboard when a user copies data from unauthorized apps) and issuing alerts to the cybersecurity team so they can investigate and respond to the incident quickly. DLP software can also furnish reporting to demonstrate cybersecurity compliance and provide evidence to auditors.
How data loss prevention works
Data loss prevention is a multi-step process that relies on a coordinated effort between several components. Each step plays a major role in the solution’s overall success in protecting an organization’s valuable data.
Real-time Discovery and Data classification “on the fly”
With the volume of data modern organizations generate today, the idea of pre-discovery and pre-classification of all data is an unwinnable game. A DLP solution for today’s distributed organization requires classification “on the fly” informed by AI and ML on the endpoint.
An organization’s data can be broadly classified into three categories:
- Low-risk data - Data classified as low-risk includes publicly available information and data that can be recovered or recreated easily.
- Moderate-risk data - This classification level consists of internal data that is important to a company but does not meet the criteria of high-risk data.
- High-risk data - Data classified as high-risk is confidential and sensitive data that should not be disclosed. It may also encompass business-critical data that cannot easily be recreated or recovered.
Three basic methods are used to classify data. In many cases, a combination of these methods is employed to ensure proper classification.
- Content-based classification uses automation to search files for sensitive information.
- Context-based classification makes use of indirect indicators to classify data. These indicators may include the information’s location, its creator, or the application that used the data.
- User-based classification relies on user knowledge to establish data sensitivity. It is a manual process that can be used to complement content and context-based classification.
Enforcing data handling policies
Today’s data protection solutions should come with policy packs that allow companies to simplify policy creation for different compliance requirements and rules for how different classes of data should be handled. For example, all high-risk data should be encrypted when at rest, in use, or in transit. It should also only be accessible to authorized individuals using approved platforms. Low-risk data can be shared and transmitted without being encrypted.
A DLP software solution automates the process of enforcing these data handling policies and remediating any issues that arise. For instance, attempts to transfer high-risk data in unencrypted form would not be permitted. Intelligent software will encrypt the data before allowing its transfer to enforce the data handling policy and ensure its security or block the sharing of data altogether, depending on your company’s rules and procedures. Another example could be enforcing a different policy for a work email account versus a personal email account.
Reporting and analysis
DLP solutions should generate reports and analytical information that can be used to optimize data handling policies and address a company’s operational deficiencies and vulnerabilities. For example, if numerous violations are discovered to be initiated by a particular department, additional training may be necessary. Similarly, if violations and warnings are being generated incorrectly, fine-tuning data classification methods may be in order.
Analytics can pinpoint the applications that make the greatest use of high-risk data and may influence how cybersecurity is implemented across an organization. Companies should embrace all possible techniques to prevent data loss, including modifying long-standing procedures that analysis shows to be inadequate in protecting sensitive enterprise information.
Employee education
Everyone in the organization should be educated regarding the risks of insecure data handling. End users need to understand how they can use data without introducing risks to the business. Employees who regularly participate in cybersecurity awareness training are less likely to accidentally expose sensitive or confidential data that can damage a company and its reputation. Modern DLP solutions should present real-time educational prompts to users when non-compliant behavior is observed.
A streamlined solution for data loss prevention
The Reveal Platform by Next is a comprehensive data loss prevention solution that is easy to use and provides organizations with immediate benefits. Reveal educates employees, discovers risks, and enforces data handling policies to prevent data loss.
Reveal provides data loss prevention with full visibility. It employs lightweight agents that detect data exfiltration attempts and perform automated policy enforcement. Machine learning and smart sensors are used to help detect risks before they become security incidents. It’s a flexible solution, compatible with Windows, macOS, and Linux machines.
Data loss prevention helps companies manage the data protection process from end to end by detecting data and creating a data inventory, classifying data based on your pre-defined rules, enforcing data handling policies to prevent risky behavior, issuing alerts to your cybersecurity team can investigate incidents quickly, and educating your employees to cultivate a cybersecurity culture. Get in touch with Next DLP and learn how your company can protect its valuable data with Reveal. You can book a demo of our efficient DLP solution or get more details on how implementing this solution can benefit your company.
Frequently asked questions
How does data loss prevention (DLP) work?
DLP follows a multi-step process that allows for the real-time discovery and safeguarding of sensitive data. All systems work differently, but most DLPs follow these steps:
- Discovery and classification: Using artificial intelligence and machine learning, DLPs classify data on the fly based on its content and context.
- Policy enforcement: The system automatically enforces data handling policies, such as encryption and access controls.
- Threat analysis: DLPs monitor and analyze data usage to detect and respond to potential security threats.
- Employee education: Some organizations outsource employee training, but many DLPs also offer ongoing training with real-time prompts that ensure compliance with data security practices.
- Alerting: All DLPs should issue alerts to cybersecurity teams for quick investigation and incident response.
What types of data does a DLP solution protect?
All types of data benefit from DLP protections, including:
- Low-risk data: This is publicly available information is easily recreated. Still, malicious actors can use this data for nefarious purposes, especially for more sophisticated social engineering attacks targeting employees.
- Moderate-risk data: This includes internal data that are important to the company but aren’t highly sensitive, such as marketing plans or production goals.
- High-risk data: Attackers primarily target sensitive, confidential data because of its value. This data is critical to business operations and shouldn’t be disclosed under any circumstances. It includes patient information in a healthcare organization, legal documents, and banking details.
How do data loss prevention platforms classify data?
There are three main ways to classify data based on:
- Content: With content-based classification, the DLP automatically scans all files for potentially sensitive information.
- Context: The DLP classifies data based on indirect information like data location, creator, or application.
- User: In user-based classification, DLP users manually classify information based on their knowledge of the data’s sensitivity.
