Data Loss Prevention (DLP) refers to a set of technologies and strategies designed to prevent the unauthorized access, sharing, or leakage of sensitive data. DLP solutions help organizations protect confidential information - such as customer data, financial records and intellectual property - by monitoring, detecting, and blocking potential data breaches or policy violations.
In Google Workspace, a built-in DLP functionality allows administrators to create rules that automatically identify and restrict the sharing of sensitive information through Gmail, Google Drive or Google Chat. For example, it can prevent employees from accidentally sending confidential data outside the organization or adding restricted data to the documents that are already shared.
Google Workspace DLP uses pre-configured or custom detectors to identify patterns that match sensitive data or confidential business information. Once identified, DLP policies can enforce specific actions, such as blocking sharing, quarantining messages, or warning users before they send sensitive content.
A schematic representation of multiple data-sharing attempts, with three attempts being blocked by Google Workspace DLP rules to prevent unauthorized data leaks.
Every company, no matter its size, deals with sensitive information - whether it’s customer data, financial records, trade secrets, or healthcare information. Without proper security tools, data leaks can happen in an instant - sometimes due to human error, like emailing the wrong person, and other times due to malicious intent, such as insider threats or cyberattacks. A single security slip-up can lead to serious consequences, such as regulatory fines and reputational damage that’s hard to recover from.
Google Workspace customers can use its DLP functionality to take a proactive approach to security by automatically detecting and preventing unauthorized sharing of sensitive data. Besides the main goal to prevent the data breaches, DLP also helps to:
Google Workspace DLP functions by scanning emails, documents, and other files for sensitive content based on configured rules. As you can see in the scheme below, each rule consists of three components: scope, condition, and action.
A visual representation of the Google Workspace DLP rule workflow illustrates how data is evaluated against predefined scope and condition criteria before a specified action, such as blocking, warning, or allowing, is applied.
One of the key aspects of configuring DLP policies effectively is scoping - determining which users and departments the policies should apply to. This is done using Organizational Units (OUs) and Groups in Google Workspace. OUs allow businesses to segment their users based on departments, job roles, or other structural divisions. When configuring DLP rules, admins can apply policies specifically to certain OUs, ensuring that security measures are tailored to different teams based on their data sensitivity and compliance needs.
Alternatively, you can use a more flexible way to apply DLP policies by targeting specific sets of users across the whole organization using Google Workspace Groups. Unlike OUs, which are hierarchical and predefined, groups can include users from different departments and locations. Additionally, Google Workspace supports the creation of groups with dynamic membership, which makes the policy assignment even more flexible.
After defining the scope, the next step is to set the conditions that must be met to trigger an action. Google Workspace DLP supports various types of conditions, including detecting specific data types, matching keywords or regular expressions, and identifying files with certain labels. A complete list of available conditions can be found in the article Create DLP for Drive rules.
To scan for specific data types, such as credit card numbers, phone numbers, or email addresses, you need to use detectors. Google Workspace provides a wide range of predefined detectors, covering the most common data protection needs. However, if the data you want to monitor is not covered by a predefined detector, you can create a custom detector. Custom detectors can be based on either word lists or regular expressions.
Regular expressions (usually referred to as regex) allow you to define specific patterns for detecting sensitive information. Explore examples of regular expressions on Google Support. For example, if you need to prevent the sharing of unique identifiers that consist of two uppercase letters followed by eight digits, you could use the following regex:
[A-Z]{2}[0-9]{8}
If your rule requires more than one condition, you can use nested conditions, which allow multiple conditions to be combined using AND, OR, and NOT operators. For example, to prevent the sharing of Social Security Numbers in documents labeled as Confidential, you need to create a nested condition with the AND operator: one condition to scan the label and one – to scan the body of the document.
Once a DLP rule condition is triggered in Google Workspace, the system takes action to prevent potential data leaks or unauthorized sharing. Depending on the service - Google Drive, Chat, or Gmail - different enforcement options are available. Actions for Google Drive and Chat include:
DLP for Gmail has similar blocking, warning and labeling actions, and contains one additional action - Quarantine message. Instead of allowing an email to be sent, this action places it in quarantine for review. An administrator or authorized reviewer can then approve or reject the message.
Additionally, you can configure notifications, so that the responsible person receives an email whenever the rule is triggered.
Gal is the Cofounder & CPO of Reco. Gal is a former Lieutenant Colonel in the Israeli Prime Minister's Office. He is a tech enthusiast, with a background of Security Researcher and Hacker. Gal has led teams in multiple cybersecurity areas with an expertise in the human element.
With growing attention to data security and increasingly strict regulations, businesses must do their best to prevent data leaks. Ensuring the protection of sensitive information is a fundamental requirement for maintaining compliance and avoiding financial or reputational damage.
Google Workspace DLP is a powerful tool that helps organizations enforce data security policies, prevent unauthorized sharing, and mitigate human errors that can lead to leaks. It not only improves security but also enhances visibility and control over data flows within your organization. However, no tool can cover 100% of potential data leaks alone, and Google Workspace DLP must be combined with other tools, such as Data Exposure Management by Reco, to improve data loss prevention. Request a demo to get the report about potential points of data exposure.
