What is Data Loss Prevention?

Data Loss Prevention (DLP) is the term for the range of procedures used to prevent data from being lost, accessed by unauthorized users or leaked, either accidentally or purposefully.

DLP solutions monitor data flowing in and out of an organization and guard sensitive data against exfiltration. This is accomplished via numerous measures such as data inspection, data encryption, threat detection, preventative measures, user education and security policies.

Data may be lost in a variety of ways.

• Malicious insiders. Insider threats are among the most dangerous types of data leaks since many security solutions fail to monitor trusted user interactions with data and applications adequately. Malicious insiders may include current and former employees, vendors, business partners and other individuals with access to network resources.
• Exfiltration. Data exfiltration is the act of transferring data from a device inside a network to an outside destination. Exfiltration often results from cyberattacks involving phishing, malware, DDoS attacks or code injection. Exfiltrated data may include intellectual property, login credentials, financial account numbers, personally identifiable information (PII) and other sensitive data.
• Negligence. Data loss is often the result of negligence or unintentional exposure. Accidental leaks may occur when employees unintentionally share sensitive information with users outside the organization via email or filesharing services or fail to encrypt files before sending. Data may also be jeopardized when physical devices like laptops or USB flash drives are lost. Misconfigured software settings, recycled passwords, and unpatched software vulnerabilities may lead to breaches that allow cyber criminals to access sensitive data quickly.

Data Loss Prevention software uses multiple tools to identify sensitive information within an IT environment, monitor data flow in and out of the organization, and block sensitive data from leaving the organization based on security policies.

DLP solutions use multiple techniques to identify potential leaks and losses.

• Identifying data. Preventing data loss begins by identifying sensitive information within the IT environment so that DLP technology knows which data assets to look for when monitoring traffic.
• Monitoring for leaks. Automated Data Loss Prevention processes identify and detect data being exfiltrated, misappropriated, or misplaced within the IT environment.
• Protecting data in motion. As data moves between locations, DLP security deploys several measures to ensure that it arrives safely at its destination.
• Securing data at rest. Data stored within databases, file systems or the cloud may be protected by endpoint Data Loss Prevention and cloud Data Loss Prevention solutions that enforce encryption policies and prevent unauthorized access.
• Protecting data in use. Data can be protected from unauthorized alteration, printing, copying, and pasting by constantly monitoring the interaction between data and users/applications.

Blocking the exfiltration of data and intellectual property to prevent unintentional leaks or data breaches is the function of Data Loss Prevention policies. A DLP policy is a set of conditions determining how users are allowed to interact with data, with options including restricting access for risky individuals or educating users on best practices. Organizations can configure DLP policies to take their country, industry and unique data risks into account, helping to ensure ongoing compliance.

A typical Data Loss Prevention policy will include the following elements:

• Type of information: Key categories of highly sensitive data include Personally Identifiable Information (PII), Protected Health Information (PHI), schematics, software code and credit card numbers.
• Severity and action: Each incident or specified number of incidents is assigned a severity level and triggers a corresponding action from a range of options including auditing, encryption and blocking.
• Users and location: A DLP policy can apply to individual users or groups of users, and Forcepoint DLP supports more flexible enforcement by applying policies to users on or off the network.
• Destination: Data may be accessed on the web, across multiple clouds or on an endpoint, sent via email or transferred to a USB, among other options.

Some vendors of DLP services make a distinction between endpoint DLP and network DLP and may even use a third term, cloud DLP. These describe complementary approaches to preventing data loss, which should be combined to provide unified, comprehensive coverage.

Endpoint DLP protects data on individual devices (“endpoints”) including servers, laptops and smartphones, traditionally via an agent installed on the device.

Network DLP protects and monitors data that is in use, in motion or at rest anywhere on the organization’s network. A critical subset of network DLP is cloud DLP, which protects data moving between the network and cloud applications.

Effective data security demands a unified DLP approach. If an endpoint is compromised, network DLP can block data from exfiltration via this threat vector. Additionally, endpoint DLP can protect sensitive data residing on a device when a network security measure such as a firewall or Virtual Private Network (VPN) fails to perform correctly.

Any organization that deals with data should concern itself with preventing leaks, but the need is especially urgent for companies holding certain kinds of data or performing certain types of work. These include:

• Personally Identifiable Information (PII), Protected Health Information (PHI) and Payment Card Information (PCI) are all subject to government regulation. In order to maintain compliance, it is critical to have a solution in place protecting customer data and a policy ensuring its proper stewardship.
• Intellectual Property (IP) is key to many companies’ core business functions and can have catastrophic consequences if not kept under control. The widespread adoption of generative AI tools makes it especially important to put measures in place guarding against accidental input of IP and trade secrets that could impact business operations.
• Bring Your Own Device (BYOD) programs have proliferated as part of the shift to remote or hybrid work models for many organizations. Recognizing that employees use their own devices to perform critical work tasks, companies should utilize DLP to protect sensitive data on endpoints from unapproved distribution.

Organizations that follow best practices of Data Loss Prevention can implement an effective DLP program more easily while minimizing the cost of Data Loss Prevention software.

• Choose a comprehensive solution. When organizations implement multiple-point solutions and adopt ad-hoc DLP policies, the inevitable result is a lack of visibility and weak security. A complete, centralized solution will significantly simplify the management of DLP programs and enhance DLP efforts.
• Ensure adequate internal resources. Managing a Data Loss Prevention plan requires personnel with expertise in data protection laws, risk analysis, breach response, training, and security awareness.
• Classify data assets. Successful Data Loss Prevention programs start by creating a classification of types of data and their value to the organization, making it easier to enforce DLP policies. Classes of data may include personally identifiable information, financial data, regulated information, intellectual property, and other types of files.
• Identify sensitive files. After creating a classification system, organizations can inventory files to identify where sensitive data resides and its associated risks. Some Data Loss Prevention solutions can help this effort by quickly searching for and cataloging data assets.
• Begin in stages. Successful Data Loss Prevention programs are built one step at a time, prioritizing the most sensitive assets and channels first.
• Establish policies. DLP policies outline how, by whom, and where sensitive data may be used. These policies allow a Data Loss Prevention solution to identify when data is being leaked or accessed by unauthorized users.
• Train employees. Education and awareness programs are essential to successful Data Loss Prevention, helping employees to understand the importance of data privacy and security and their role in implementing DLP best practices.
   

As users make sanctioned and unsanctioned use of generative AI (GenAI) tools to increase productivity and improve processes, there are increased risks of sensitive or proprietary information being improperly input. If used to train Large Language Models (LLMs), this information is at risk of resurfacing online and leading to a data breach.  Organizations face the challenge of preventing the wrong kinds of data from being exfiltrated, and preventative measures will generally entail both blocking access to unapproved Gen AI websites and vetting tools that can ensure the proper stewardship of sensitive information.

Data Loss Prevention solutions can be powerful resources for guarding against improper Gen AI use. An employee who attempts to circumvent security measures by copying and pasting sensitive information to send to another device can be stopped by an appropriately configured DLP policy. DLP can also ensure that the proper security measures are in place before approved GenAI use occurs; for instance, it can block users from entering information into tools like ChatGPT or Microsoft Copilot in public mode, prompting them to log in to the enterprise account where the correct security settings apply.

Data loss threats are generally accompanied by warning signs, indicating either that a user has bad intentions or that they are working with a compromised device or account. But these warning signs can be hard to spot, and closely monitoring user behavior can generate too much work for security teams. That is why traditional DLP is best complemented by a risk-adaptive solution that can apply advanced behavioral analysis at scale, identifying telltale signs of risk. This type of solution works by dynamically assigning risk levels to individual users and automatically adjusting privileges to match, ensuring that a hostile or compromised user cannot distribute sensitive information outside the organization.

By employing a solution such as Forcepoint Risk-Adaptive Protection, organizations can respond more accurately and rapidly to insider threats. At the same time, this solution eliminates false positives to increase operational efficiency and allows low-risk users to work in privacy and without interruption.

As a leading user and data security company, Forcepoint offers Data Loss Prevention solutions built for today’s most challenging data security risks. Forcepoint DLP enables businesses to intuitively discover, classify, monitor, and protect data while adding zero friction to the user experience.

With Forcepoint DLP, security teams can:

• Streamline Data Loss Prevention by replacing broad, sweeping rules with individualized, adaptive data security that doesn’t add friction to the user experience.
• Simplify compliance by viewing and controlling all data with the industry’s most extensive library of pre-defined policies.
• Ensure compliance with data privacy regulations across 80+ countries for GDPR, CCPA, and more. 
• Protect critical intellectual property with greater accuracy.
• Track interaction with intellectual property in both structured and unstructured forms.
• Prevent low and slow data theft even when user devices are off the network.
• Automatically block actions based on a user's risk level with risk-adaptive data protection.

Forcepoint DLP offers additional capabilities that are unavailable in Data Loss Prevention for Google or Data Loss Prevention in Office 365. Forcepoint enables teams to customize DLP policies by geography and industry and to use granular data fingerprinting within documents and database records. Forensic data can be stored with encryption, and Forcepoint can detect data hidden in images with Optical Character Recognition (OCR).