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Understand what Data Loss Prevention (DLP) is and explore the various types of DLP solutions to ensure robust data security in your business.
What is Data Loss Prevention DLP?
Security and technology protect sensitive data from loss, theft, and misuse in data loss prevention (DLP). Many companies use data to differentiate themselves. Most enterprise networks contain sensitive data including sales records, trade secrets, and consumer personal information. Organizations frequently struggle to keep their vital data safe, and hackers target this data.
Meanwhile, company data is accessed daily by hundreds, if not thousands, of authorized individuals across on-premises repositories and cloud storage. Most organizations priorities preventing data loss while allowing authorized access.
By monitoring data throughout the network and implementing security controls on that data, data loss prevention (DLP) assists organizations in preventing data losses and breaches. Security teams work to make sure that only the appropriate individuals have access to the appropriate data for the appropriate purposes.
As data packets travel over a network, a DLP solution examines them to look for the usage of private data, including credit card numbers, medical records, customer information, and intellectual property. In this manner, businesses can implement appropriate usage guidelines and access restrictions for any kind of data.
Why is dlp important
Information protection is a top responsibility for an organization since data is vulnerable no matter where it is kept. Failure can come at a great cost. The average cost of a data breach worldwide grew 10% to USD 4.88 million in the past year, the highest increase since the pandemic, according to IBM’s latest Cost of a Data Breach Report.
Particularly useful to criminals and frequently targeted is personally identifiable information (PII). Nearly half of the breaches involved consumer PII, which might include home addresses, phone numbers, emails, and tax identification (ID) numbers, according to the Cost of a Data Breach Report. With 43% of breaches, intellectual property (IP) records came in second.
Because an organization’s data may be utilized or stored in many formats, in multiple places, by multiple stakeholders across organizations, data protection is becoming more and more challenging. Furthermore, depending on sensitivity levels or applicable data privacy laws, different data sets may need to adhere to different guidelines.
By keeping an eye on all data across the network in all three states in use, in motion, and at rest—DLP policies and technologies assist organizations in protecting themselves.
- Data in use: When data is accessed, processed, updated, or removed, it is said to be in use. For instance, data from an organization utilized for computations or analysis, or a text document that an end user has altered.
- Data in motion: Also referred to as data in transit, this refers to data that is transferred across networks or moving through a network, for example, via a messaging app or an event streaming server. Of the three states, data in motion is the least secure and needs extra care.
- Data in storage: Such as on a local hard drive, cloud drive, or archive, is referred to as data at rest. Although protecting data at rest is generally simpler, security precautions must still be taken. Even something as easy as someone taking a USB flash drive from an unattended workstation can endanger data that is at rest.
The ideal data loss prevention system for an organization should be able to keep track of all data both at rest and in motion for every type of software that is being used. For instance, including DLP protection for email, teamwork, corporate intelligence (BI) apps, archiving, and operating systems like Microsoft Windows and macOS.
How does data loss prevention work?
With the aid of DLP systems, security teams usually implement DLP policies in four steps over the course of the data lifecycle:
- Identification and categorization of data
- Data tracking
- Using data protection measures
- Documenting and reporting DLP efforts
Identification and categorization of data
The company first catalogues all of its data, both organized and unstructured.
- Data with a standardised format, such a credit card number, is called structured data. Usually, it has a distinct label and is kept in a database.
- Free-form information, such written documents or pictures, that might not be neatly arranged in a central database is known as unstructured data.
DLP tools are commonly used by security teams to scan the whole network and find data stored anywhere, including on employees’ personal devices, in the cloud, and on physical endpoint devices.
The company then categorizes this data, grouping it according to shared traits and sensitivity degree. The organization can apply the appropriate DLP policies to the appropriate types of data by classifying the data.
For instance, some businesses may classify data according to its kind, such as marketing, financial, or intellectual property data. Other organizations may classify data according to applicable laws, including the California Consumer Privacy Act (CCPA) or the General Data Protection Regulation (GDPR).
Data classification can be automated with several DLP solutions. These technologies analyze both structured and unstructured data using artificial intelligence (AI), machine learning, and pattern matching to identify the type of data, its level of sensitivity, and the applicable policies.
Data tracking
The security team keeps an eye on how data is handled once it has been classified. DLP tools can detect and monitor the use of sensitive data using a variety of methods. These methods consist of:
- Content analysis, such as parsing an email message for private information using AI and machine learning.
- Data matching, such checking the contents of a file against known sensitive data.
- Identifying metadata, such as tags and labels, that specifically designate a file as sensitive. referred to as “data fingerprinting” at times.
- File matching, in which the hashes the file identities of protected files are compared by a DLP program.
- Keyword matching is the process by which DLP searches for terms frequently present in sensitive data.
- Pattern matching includes searching for information that adheres to a specific format. For instance, an American Express card will always start with “3” and contain a 15-digit number. However, not all of these figures pertain to AmEx; therefore, a DLP solution may additionally search for the company name, acronym, or expiration date in the vicinity.
A DLP tool searches for policy breaches, unusual user behaviour, system vulnerabilities, and other indications of possible data loss when it discovers sensitive material. These indicators include:
- Data leaks, like when a user tries to give someone outside the company access to a private file.
- Unauthorised users trying to access important information or carry out prohibited activities, like altering, deleting, or copying a private file.
- Traffic from unidentified devices, malware signatures, or other signs of harmful behaviour.
Using data protection measures
DLP solutions provide the ability to take immediate corrective action when they identify policy infractions. Among the examples are:
- Data transmission across the network is encrypted.
- Stopping illegal access to information.
- Preventing harmful traffic and illegal transfers.
- Notifying users that they are breaking the rules.
- Highlighting questionable activity for the security staff to investigate.
- Posing additional authentication difficulties before allowing users to access important info.
- Implementing least-privilege resource access, for example, in a zero-trust setting.
- By automatically backing up data so that it may be restored following a loss, certain DLP technologies also aid in data recovery.
Additionally, organizations can enforce DLP policies more aggressively. Role-based access control policies and other forms of effective identification and access management (IAM) can limit who has access to data. Employees can assist stop unintentional data loss and leaks before they happen by receiving training on data security regulations and best practices.
Documenting and reporting DLP efforts
Security teams employ dashboards and reporting features found in DLP products to keep an eye on sensitive data across the network. The security team can use this documentation to monitor the performance of the DLP program over time and make necessary adjustments to policies and methods.
By documenting their data security initiatives, DLP solutions can also assist organizations in adhering to pertinent requirements. These documents can be used by the organization to demonstrate that it complied with the proper data handling protocols in the event of a cyberattack or audit.
Types of DLP Solutions
DLP solutions are used by organizations to keep an eye on network activity, identify and tag data, and enforce DLP regulations to stop theft or misuse.
Three primary categories of DLP solutions exist:
- Networks DLP
- Endpoint DLP
- Cloud DLP
Networks Data loss prevention
Data flow through, into, and out of a network is the main emphasis of network DLP systems. They frequently employ machine learning (ML) and artificial intelligence (AI) to identify unusual traffic patterns that could indicate a data loss or leak. Many network DLP technologies provide visibility into data that is in use and at rest on the network, even if their primary purpose is to monitor data in motion.
Endpoint Data loss prevention
Endpoint DLP systems keep an eye on activity on servers, laptops, mobile devices, and other network-accessing devices. These programs can prevent users from engaging in illegal activity on the devices they monitor because they are installed directly on the devices. Additionally, certain endpoint DLP tools prevent unauthorized device-to-device data transfers.
Cloud Data loss prevention
Data stored in and accessed by cloud services is the main focus of cloud security solutions. They are able to monitor, categories, scan, and encrypt data stored in cloud repositories. Enforcing access control restrictions for both individual end users and any cloud services that may have access to company data is another benefit of these technologies.
Depending on their needs and the way they keep their data, organizations may decide to employ a single solution or a combination of different solutions. The objective is still the same: to protect all sensitive information.