DLP tools typically feature dashboards and reporting functions that security teams use to monitor sensitive data throughout the network. This documentation enables the security team to track DLP program performance over time so that policies and strategies can be adjusted as needed.

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Any digital device left unattended—on a desk, car or bus seat—can be a tempting target and grant the thief access to a network and permission to access data. Even if the thief only wants to sell the equipment for cash, the organization still suffers the disruption of shutting off access to that device and replacing it.

Protecting data is becoming ever more difficult because an organization’s data might be used or stored in multiple formats, in multiple locations, by various stakeholders across organizations. Moreover, different sets of data might need to follow different rules based on sensitivity levels or relevant data privacy regulations.

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For example, some organizations might group data based on type, such as financial data, marketing data or intellectual property. Other organizations might group data based on relevant regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).

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When a DLP tool finds sensitive data, it looks for policy violations, abnormal user behavior, system vulnerabilities and other signs of potential data loss, including:

From the rise of generative AI to emerging regulations, several factors are changing the data landscape. In turn, DLP policies and tools will need to evolve to meet these changes. Some of the most significant trends in DLP include:

Also, data protection policies can enhance operational efficiency by offering clear processes for data-related activities such as access requests, user provisioning, incident reporting and security audits.

With major data breaches and social media abuses come increased calls for government and industry regulation, which can add to the complexity of systems and compliance verifications. Recent developments such as the EU AI Act and the CCPA draft rules on AI are imposing some of the strictest data privacy and protection rules to date.

Data in use: This is when data is accessed, processed, updated or deleted. For example, an organization’s data used for analysis or calculations or a text document edited by an end user.

Some losses arise from simple mistakes, while others are caused by cyberattacks such as distributed denial of service (DDos) attacks and phishing. Almost any data loss can cause significant business disruptions.

DLP strategies are often aligned with compliance efforts. Many organizations craft their DLP policies specifically to comply with regulations such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), the Health Insurance Portability and Accountability Act (HIPAA) and the Payment Card Industry Data Security Standard (PCI DSS).

Large language models (LLMs) are, by definition, large, and they consume massive amounts of data that organizations must store, track and protect against threats such as prompt injections. Gartner has forecast that “By 2027, 17% of the total cyberattacks/data leaks will involve generative AI.”1

A major benefit of data protection policies is that they set clear standards. Employees know their responsibilities for safeguarding sensitive information and often have training on data security practices, such as identifying phishing attempts, handling sensitive information securely and promptly reporting security incidents.

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On the other hand, human error might be as simple as leaving a smartphone at a cash register or deleting files by mistake.

Malicious insiders are often motivated by personal gain or a grievance toward the company. Insider threats can be unintentional and as simple as the carelessness of not updating passwords, or as dangerous as exposing sensitive enterprise data while using publicly available generative AI (gen AI).    Malicious insider attacks are common and costly. The latest Cost of a Data Breach Report from IBM found that compared to other vectors, malicious insider attacks resulted in the highest costs, averaging USD 4.99 million.

Cloud security solutions focus on data stored in and accessed by cloud services. They can scan, classify, monitor and encrypt data in cloud repositories. These tools can also help enforce access control policies on individual end users and any cloud services that might access company data.

Ransomware is a form of malware that threatens to destroy or withhold the victim’s data or files unless a ransom is paid to the attacker.

Managing data within a building or network is simpler than providing system access to a mobile workforce or remote workers, where the communication and access issues multiply the efforts required of the IT staff.

This includes passwords that hackers can easily guess, or passwords or other credentials—for example, ID cards—that hackers or cybercriminals might steal.

Ideally, an organization’s data loss prevention solution is able to monitor all data in use, in motion and at rest for the entire variety of software in use. For example, adding DLP protection for archiving, business intelligence (BI) applications, email, teaming and operating systems such as macOS and Microsoft Windows.

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Both kinds of data need to be protected, but in different ways; hence, distinct DLP policies tailored to each type of data are needed.

Rather than drafting a single policy for all data, information security teams typically create different policies for the different types of data in their networks. This is because different types of data often need to be handled differently for different use cases to meet compliance needs and avoid interfering with the approved behavior of authorized end users.

Data in motion: Also known as data in transit, this involves data moving through a network, such as being transmitted by an event streaming server or a messaging app, or moved between networks. Data in motion is the least secure of the three states and requires special attention.

Next, the organization classifies this data, sorting it into groups based on sensitivity level and shared characteristics. Classifying data enables the organization to apply the right DLP policies to the right kinds of data.

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Network DLP solutions focus on how data moves through, into and out of a network. They often use artificial intelligence (AI) and machine learning (ML) to detect anomalous traffic flows that might signal a data leak or loss. While network DLP tools are designed to monitor data in motion, many also offer visibility into data in use and at rest on the network.

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Data loss prevention (DLP) helps organizations stop data leaks and losses by tracking data throughout the network and enforcing security policies on that data. Security teams try to ensure that only the right people can access the right data for the right reasons.

Organizations can take more proactive measures to enforce DLP policies as well. Effective identity and access management (IAM), including role-based access control policies, can restrict data access to the right people. Training employees on data security requirements and best practices can help prevent accidental data losses and leaks before they happen.

Data exfiltration: Exfiltration refers to stealing data. This is any theft when an attacker moves or copies someone else’s data to a device under the attacker’s control. All data exfiltration requires a data leak or a data breach, but not all data leaks or data breaches lead to data exfiltration.

Organizations might choose to use one type of solution or a combination of multiple solutions, depending on their needs and how their data is stored. The goal for all remains clear: to defend all sensitive data.

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In addition, remote workers sometimes have multiple employers or contracts, so that “crossed wires” can create more data leaks. Gartner predicts that “by the end of 2026, democratization of technology, digitization and automation of work will increase the total available market of fully remote and hybrid workers to 64% of all employees, up from 52% in 2021.”1

DLP policies can cover multiple topics, including data classification, access controls, encryption standards, data retention and disposal practices, incident response protocols and technical controls such as firewalls, intrusion detection systems and antivirus software.

After data is classified, the security team monitors how it is handled. DLP tools can use several techniques to identify and track sensitive data being used. These techniques include:

Data breach: A data breach is any security incident that results in unauthorized access to confidential or sensitive information. This includes any cyberattack or other security incident in which unauthorized parties gain access to sensitive data or confidential information.

DLP tools can also help organizations comply with relevant regulations by keeping records of their data security efforts. If there is a cyberattack or audit, the organization can use these records to prove that it followed the appropriate data handling procedures.

In the meantime, hundreds, if not thousands, of authorized users access enterprise data across cloud storage and on-premises repositories every day. Preventing data loss while facilitating authorized access is a priority for most organizations.

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Structured and unstructured data are sourced, collected and scaled in different ways and each one resides in a different type of database.

A DLP solution inspects data packets as they move across a network, detecting the use of confidential information such as credit card numbers, healthcare data, customer records and intellectual property. This way, organizations can apply the right access controls and usage policies to each type of data.

However, the company might do what it wishes with its own intellectual property (IP). Furthermore, the people who need access to PII might not be the same people who need access to company IP.

Data leakage: This is the accidental exposure of sensitive data or confidential information to the public. Data leakage can result from a technical security vulnerability or procedural security error and can include both electronic and physical transfers.

Many organizations now store data on premises and in multiple clouds, possibly even in multiple countries. These measures might add flexibility and cost savings, but they also increase the complexity of protecting that data.

Data at rest: This is data in storage, such as sitting in a cloud drive, local hard disk drive or archive. Generally, data at rest is easier to protect, but security measures still need to be in place. Data at rest can be compromised through an act as simple as someone picking up a USB flash drive from an unattended desk.

Data thieves use tactics that fool people into sharing data they shouldn’t share. Social engineering can be as artful as a phishing attack that convinces an employee to email confidential data, or as devious as leaving a malware-infected USB flash drive where an employee might find it and plug it into an organization-supplied device.

Many DLP solutions include prewritten DLP policies aligned to the various data security and data privacy standards companies need to meet.

Security teams typically use DLP tools to scan the entire network to discover data wherever it is stored—in the cloud, on physical endpoint devices, on employees' personal devices and elsewhere.

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Data is at risk regardless of where it is stored, making information protection a significant priority for an organization. The cost of failure can be high. The latest Cost of a Data Breach Report from IBM® found that the global average cost of a data breach increased 10% over the previous year, reaching USD 4.88 million, the biggest jump since the pandemic.   Personally identifiable information (PII), in particular, is highly valuable to thieves and often targeted. The Cost of a Data Breach Report also found that nearly half of all breaches involved customer PII, which can include tax identification (ID) numbers, emails, phone numbers and home addresses. Intellectual property (IP) records came in a close second with 43% of breaches.

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Endpoint DLP tools monitor activity on laptops, servers, mobile devices and other devices accessing the network. These solutions are installed directly on the devices that they monitor, and they can stop users from committing prohibited actions on those devices. Some endpoint DLP tools also block unapproved data transfers between devices.

Depending on how well backed up an organization’s data is, a hard disk drive malfunction might be catastrophic. The cause might be a head crash or software corruption. Spilling a refreshing beverage in the office—coffee, tea, soda or water—might short-circuit the system board in a PC, and there’s hardly ever a convenient time. An interruption in the power supply can shut down systems at the wrong or worst time, which then might interrupt the saving of work or break transmissions.

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Data is a competitive differentiator for many businesses. A typical corporate network contains a trove of trade secrets, sales records, customers' personal data and other sensitive information. Hackers target this data, and organizations often struggle to keep their critical data secure.

Employees might be sharing work files on a personal cloud storage account, meeting on an unauthorized video conferencing platform or creating an unofficial group chat without IT approval. Personal versions of Dropbox, Google Drive and Microsoft OneDrive might create security headaches for the IT team.

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Authorized users—including employees, contractors, stakeholders and providers—might put data at risk through carelessness or malicious intent.

DLP policies and tools help organizations protect themselves by monitoring every piece of data throughout the network in all three states: in use, in motion and at rest.

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For example, the Cost of a Data Breach Report found that 40% of breaches occur at organizations that store their data across multiple environments.

Data loss events are often described as data breaches, data leakage or data exfiltration. The terms are often used interchangeably, but they have distinct meanings.

This is software created specifically to harm a computer system or its users. The best-known form of data-threatening malware is ransomware, which encrypts data so that it can’t be accessed and demands a ransom payment for the decryption key. Sometimes, attackers will even ask for a second payment to prevent the data from being exfiltrated or shared with other cybercriminals.

Vulnerabilities are weaknesses or flaws in the structure, code or implementation of an application, device, network or other IT asset that hackers can exploit. These include coding errors, misconfigurations, zero-day vulnerabilities (unknown or as yet unpatched weaknesses) or out-of-date software, such as an old version of MS Windows.

Data loss prevention (DLP) is the discipline of shielding sensitive data from theft, loss and misuse by using cybersecurity strategies, processes and technologies.

For example, personally identifiable information (PII)—such as credit card numbers, social security numbers and home and email addresses—is subject to data security regulations that dictate proper handling.

Different regulations impose different standards for different kinds of data. For example, HIPAA sets rules for personal health information, while PCI DSS dictates how organizations handle payment card data. A company that collects both kinds of data would likely need a separate DLP policy for each kind to meet compliance requirements.

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Organizations use DLP solutions to monitor network activities, identify and tag data and enforce DLP policies to prevent misuse or theft.

Organizations are also dealing with an increase in shadow data—that is, data in the enterprise network that the IT department does not know about or manage. The proliferation of shadow data is a major contributor to data breaches. According to the Cost of a Data Breach Report, 35% of breaches involve shadow data.

Many DLP solutions can automate data classification. These tools use AI, machine learning and pattern matching to analyze structured and unstructured data to determine what type of data it is, whether it is sensitive and which policies should apply.

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Security teams typically follow a 4-step process throughout the data lifecycle to put DLP policies into practice with the help of DLP tools: