One-stop Guide for Protecting Your Organization’s Sensitive Data Using Data Loss Prevention (DLP) Tools
What Is Data Loss Prevention?
Data loss prevention (DLP) is a set of tools and processes to prevent sensitive or confidential data from being lost, stolen, or accidentally leaked.
You can use DLP technologies on sensitive information assets like customer records, financial records, intellectual property, and contracts to prevent this confidential information from leaving the organization without permission, which could lead to a costly data breach and compliance violation.
You can apply DLP solutions at the network level, on individual computers or devices, or within specific applications. Read Kiteworks’ 2023 Forecast for data privacy exposure risk, including data loss prevention.
DLP is often used by an organization’s cybersecurity, governance, risk, and compliance teams to help protect the organization’s data and ensure compliance with data protection regulations.
Typical Cases Where Companies Use DLP Tools to Protect Their Data:
- To protect sensitive or confidential information from being lost, stolen, or accidentally leaked
- To comply with data protection laws, such as the General Data Protection Regulation (GDPR) in the European Union or the Health Insurance Portability and Accountability Act (HIPAA) in the United States
- To protect intellectual property and proprietary business information from being accessed or shared without authorization
- To monitor and control the movement of sensitive data within an organization, including preventing the unauthorized transfer of data to external devices or to prevent unauthorized sharing of data via email or other unauthorized communication channels
- To detect and prevent insider threats, such as employees who may intentionally or accidentally leak sensitive data. Watch these Kitetoons animated videos to learn what can happen when employees access and recklessly share sensitive business information without data loss prevention or other security solutions and procedures.
Top 5 Trends Driving DLP Adoption in 2023
- The Increasing Amount of Sensitive Data Being Created and Stored: As more organizations adopt digital technologies and workflows, they create more data and much of it is highly sensitive. The more data, the higher the risk of data loss or theft.
- The Rise of Cyberattacks and Data Breaches: Hackers and cybercriminals are constantly finding new ways to access, steal, and monetize sensitive data, so it’s absolutely critical that organizations be proactive in their data protection efforts.
- Increased Regulatory Pressure: Data protection regulations, such as the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States, have placed additional pressure on organizations to implement robust DLP measures to ensure compliance or face costly fines and penalties.
- The Growth of Cloud Computing and Remote Work: As more organizations adopt cloud-based systems and allow employees to work remotely, it becomes more challenging to monitor, control, and protect the movement of sensitive data.
- The Proliferation of Mobile Devices: The widespread use of smartphones and other mobile devices has made it easier for employees to access and share sensitive data. DLP solutions that can monitor and control data access on mobile devices have therefore become increasingly important.
Top Causes of Data Leaks, Including Examples:
1) Human Error: Employees, especially when rushed or distracted, are prone to making mistakes that can often cause data leaks. Example: An employee at a financial institution accidentally sends an email containing a list of clients’ Social Security numbers to a group of unauthorized individuals instead of the intended recipient.
2) Malicious Insiders: Disgruntled employees, in some cases, can intentionally leak data for personal gain or to cause harm to the organization.
Example: An angry healthcare employee passed over for a promotion intentionally leaks patient medical records to a news outlet to damage his employer’s reputation.
3) Cyberattacks: Hackers and cybercriminals may use a variety of tactics, such as phishing attacks or malware infections, to gain access to sensitive data and leak it publicly or sell it on the black market.
Example: A criminal organization hacks into a government agency’s database, downloads terabytes of sensitive data, and sells it to a foreign government.
4) Unsecured Data Storage: Lack of proper security controls to limit access to data storage systems may result in data leakage if accessed by unauthorized individuals.
Example: A small business stores its customers’ credit card numbers in an unencrypted file on a misconfigured or unprotected server, which anyone can access with just an internet connection.
Top 10 Best Practices to Protect Your Organization’s Sensitive Data Using Data Loss Prevention (DLP)
- Develop and Implement a Comprehensive DLP Strategy: This should include identifying the types of data that must be protected, establishing policies and procedures for protecting that data, and determining the technologies and tools needed to support those policies.
- Encrypt Sensitive Data: Encrypting data makes it much more difficult for unauthorized individuals to access and read it.
- Use Access Controls and Authentication: Implementing strong access controls, such as unique user accounts and passwords, can help prevent unauthorized access to sensitive data.
- Monitor and Track Data Access: Use monitoring tools to track and log access to sensitive data so that any potential data loss events can be detected and addressed quickly.
- Train Employees on Data Protection: Ensure that all employees know their role in protecting sensitive data and provide the necessary training and resources.
- Regularly Review and Update DLP Policies: As technology and the threat landscape evolve, it’s essential to periodically review and update your DLP policies and procedures to ensure they are still effective.
- Implement Technical Controls: Use technical controls, such as firewalls and antivirus software, to help prevent data loss from cyberattacks or other security threats.
- Secure Data Storage: Properly secure data storage locations and ensure that sensitive data is encrypted whenever possible.
- Use Data Classification: Implement a system for classifying data based on its sensitivity so that you can apply appropriate security measures.
- Conduct Regular Risk Assessments: Regularly assess your organization’s sensitive data risks and implement appropriate controls to mitigate those risks.
Components of a Data Loss Prevention (DLP) Solution
Data Classification: Data classification is the process of categorizing data based on its sensitivity or importance. Once you classify data by sensitivity, you can apply appropriate security measures and controls for the data. For example, an organization may classify financial or customer personal data as highly sensitive and apply stricter security measures to protect them.
Data classification schemes can vary depending on the needs of the organization, but common categories might include “confidential,” “private,” “restricted,” and “public.” Data classification can help organizations ensure that they comply with cybersecurity frameworks and data protection regulations, and ensure their sensitive data is adequately protected.
Data Discovery: Data discovery identifies sensitive data within an organization’s infrastructure. It can identify data stored on servers, laptops, and other devices, as well as data transmitted over networks or stored in cloud-based systems.
Data discovery tools can help organizations locate and inventory their sensitive data so that they can apply appropriate security measures and controls to protect it. Data discovery is a critical component of a data loss prevention (DLP) solution, as it helps organizations to identify and prioritize their most valuable or sensitive data and ensure that it is adequately protected.
Data Monitoring: Data monitoring is the process of tracking and monitoring access to and movement of sensitive data within an organization. It can include monitoring data access on servers, laptops, and other devices and data transmitted over networks or stored in cloud-based systems.
Data monitoring is another critical component of a data loss prevention (DLP) solution. It helps organizations detect and respond to potential data loss events in real time and protect their sensitive data from unauthorized access or disclosure. Data monitoring tools can help organizations see possible data loss events, such as unauthorized access to or transfer of sensitive data, and alert them to take action to prevent or minimize data loss from occurring.
Data Protection: Data protection refers to the measures and controls to prevent sensitive or confidential data from being lost, stolen, or accidentally leaked. It can include things like encryption, access controls, and monitoring systems. Data protection can be implemented at various points in an organization’s infrastructure, such as at the network level, on individual computers or devices, or within specific applications.
Data protection is yet another critical component of a data loss prevention (DLP) solution, as it helps organizations protect their sensitive data and ensure compliance with data protection regulations.
Data Loss Prevention Policies: Data loss prevention (DLP) policies are procedures, processes, and guidelines put in place to prevent data loss events from occurring within an organization. These policies may cover topics such as how sensitive data should be stored, transmitted, and accessed, and may include technical controls such as encryption or access controls.
By establishing clear DLP policies and enforcing them consistently, organizations can effectively prevent data loss.
Data Loss Prevention Training: Employee training programs are designed and provided to educate an organization’s workforce on the dangers and repercussions of data loss, data protection best practices, and the employee’s role in preventing data loss.
Data Loss Prevention Reporting: Data loss prevention (DLP) reporting refers to the use of tools and systems to generate reports on data loss events and the effectiveness of a DLP solution. These reports can help organizations understand the types of data loss risks, events, root causes, and their impact on the organization.
DLP reporting can also help organizations track the effectiveness of their DLP policies and procedures and identify areas where they may need to be revised or improved. By regularly reviewing DLP reports, organizations can gain valuable insights into their data protection efforts and take action to improve them.
Data Loss Prevention Integration: DLP can be implemented as a standalone solution or integrated into other security systems, such as email servers, file servers, and cloud storage systems.
There are several ways to integrate DLP into an organization’s security infrastructure. One common approach is to use DLP software installed on servers, workstations, and other devices to monitor and control the flow of sensitive data. You can configure this software to identify sensitive data based on keywords, patterns, or other characteristics and to take specific actions when such data is detected, such as blocking the transmission of the data or generating an alert.
Another approach to DLP integration is to use network-based DLP solutions, which monitor data as it flows across the network and can take action to prevent the loss or leakage of sensitive data. You can implement these solutions at different points in the network, such as at the firewall, the network perimeter, or critical issues.
Data Loss Prevention Support: Data loss prevention (DLP) support refers to the technical support and maintenance provided for a DLP solution. It can include troubleshooting and problem-solving assistance, updates and patches, and ongoing maintenance to ensure that the DLP solution is functioning correctly.
DLP support may be provided by the vendor of the DLP solution or by an in-house IT team. By providing ongoing support and maintenance for their DLP solution, organizations can ensure that it functions optimally and provides the necessary protection for their sensitive data.
Conclusion: Data loss prevention (DLP) is critical to any organization’s data protection strategy. By implementing a comprehensive DLP solution, organizations can prevent sensitive or confidential data from being lost, stolen, or accidentally leaked, ensuring that their data is protected and their business complies with relevant data protection regulations.
A robust DLP solution should include components such as data classification, data discovery, data monitoring, and data protection, as well as policies, training, reporting, and integration with other security systems. By following best practices and regularly reviewing and updating their DLP policies and procedures, organizations can effectively protect their data and minimize the risk of data loss events.