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Magda On Cyber

3 Things to Consider before Implementing Data Loss Prevention

Co-Written with Tom Philippe

Data Loss Prevention (DLP) refers to processes and tools designed to prevent sensitive data from being lost or stolen.

Companies dealing with large amounts of confidential data are always looking for ways to prevent data loss. Data Loss Prevention (DLP) is a strategy used to detect potential security risks and prevent the unauthorized sharing of sensitive information.

There are a variety of ways that data can be lost or stolen, so DLP includes a range of measures aimed at preventing various types of data loss. For example, DLP might involve ensuring that all sensitive data is encrypted, that backups are made regularly, and that only authorized users have access to sensitive data. The goal of DLP is to protect organizations from the financial and reputational damage that can occur if sensitive data is lost or stolen.

There are many benefits of using DLP, including:
— protecting against data breaches and leaks;
— complying with industry regulations and standards;
— reducing the chances of human error;
— minimizing the impact of malicious threats;
— and more.

Data Loss Prevention can be deployed in several ways, depending on the needs of the organization. Common methods include content filtering, user activity monitoring, access control, encryption, and others.

Photo by Markus Spiske on Unsplash

1. Understand what data you need to protect

Before implementing a DLP, it is critical to determine what data you need to protect, where the data is stored, how the data is used, and who uses or gets the data before moving further.

One of the first steps in understanding what data you need to protect is to determine what personal data is being collected and used. Depending on the country or region, there may be different legal or regulatory requirements for the type of data that must be protected. In general, however, personal data includes any information that could be used to identify an individual person. This might include a person’s name, address, email address, phone number, financial information, medical records, or other sensitive information.

Once you have identified the personal data being collected and used, you can start to assess what risks are associated with that data. Data protection laws and regulations typically require companies to take reasonable security measures to protect personal data from unauthorized access and disclosure.

Particular attention should be paid to the type of sensitive information (PHI, PII, PCI DSS, and so on), as well as how it is transferred from one system to another, as there are a number of personal data legal regulatory requirements that businesses must comply with when it comes to the storage and handling of sensitive information, such as PHI and PII.

Additionally, businesses must comply with any specific regulations that apply to the type of sensitive information they are dealing with. So for PHI, businesses must comply with HIPAA regulations, while for PII they must comply with GDPR regulations when they deal with European Residents’ personal data.

However, it is critical to understand that DLP tools will not be able to detect and protect all type of data.

The biggest limitation of data loss prevention (DLP) tools is that they are mostly designed to work with structured data sources, like databases. They often cannot handle unstructured data sources, like source code, very well. This can lead to false positives or false negatives when trying to detect sensitive data in these types of files.

2. Define data handling policies

Data handling policies are needed for DPL solutions to make sure that the data is kept secure and confidential, based on the company’s requirements. Without these policies, it would be difficult to guarantee the security and integrity of the data.

DPL solutions need to be able to handle large amounts of data quickly and accurately. This requires a high degree of data quality control. The solutions need to ensure the data is accurate, up-to-date, and compliant with regulations.

In addition, DPL solutions need to protect the confidentiality and integrity of the data they use, as per the company’s requirements, which are different depending on where the company operates, the industry, and many other aspects. Data handling policies help to ensure that these security requirements are met.

There are a number of different data handling policies that can be implemented within a DLP solution, depending on the specific needs and requirements of the organization.

Some of the most common policies include:

  1. Restrictions on who can access certain data: This policy might stipulate that only certain individuals or groups within the organization are able to access sensitive data, in order to limit its exposure.
  2. Controls on how data can be used: This policy might place restrictions on what individuals can do with sensitive data once they have accessed it, in order to prevent unauthorized use or disclosure.
  3. Encryption of sensitive data: This policy ensures that sensitive data is encrypted when it is stored or transmitted.
  4. Controls on how much data can be shared: This policy can limit the number of PII shared outside an organization.

3. Detect first, before blocking

Finally, the deployment of a DLP system is not a precise science in the traditional sense. Each organization has its own set of criteria as well as its own set of internal procedures.

As a result, fine tuning is necessary, but it will not be completed overnight.
The implementation of the DLP solution should be done in stages so that it does not interfere with critical company operations. As with any new software implementation, there are a number of factors to consider when deciding whether or not to go ahead with a DLP solution.

One key factor is the size and complexity of your organization — if you have a large, complex organization with multiple systems and data sources, it may be more difficult to implement a DLP solution all at once. In this case, implementing the solution in stages may make more sense, gradually rolling it out to different parts of the organization as each stage is completed. Another key factor is budget — if you’re working with limited resources, implementing a DLP solution in stages may be the only way to afford the project. Fine tuning the data handling policies requires time and resources.

Lastly, when adopting such a system, it is recommended that detection rules be implemented first, rather than blocking rules. Business processes may continue in a transparent manner without being slowed down, and policies can be fine-tuned in this manner. The blocking mode may be activated after the rules have been appropriately developed to ensure that they correspond to corporate data handling norms while not interfering with business processes.

Photo by Rob Wicks on Unsplash

Data loss prevention limitations

As mentioned before, the biggest limitation of data loss prevention tools is that they can’t always identify unstructured data sources, such as source code. This is a major concern for companies that develop software or handle other sensitive information that needs to be protected from accidental or intentional disclosure.

Data loss prevention tools are becoming more sophisticated, but they still rely on certain patterns (e.g., keywords) to identify sensitive information. When data is stored in an unstructured format, it can be difficult to detect these patterns, which means the data may not be properly protected.

Another limitation is that DLP tools can be quite complex to configure and manage, making them difficult to use for many organizations. Finally, DLP tools can sometimes be circumvented by malicious users who know how to bypass the detection mechanisms.

There are a number of data loss prevention tools available on the market today. However, each of these tools has its own limitations.

Implementing a DPL solution can be tricky and there are a few common mistakes that organizations make. First, they often underestimate the complexity of the problem they’re trying to solve and think that a simple DPL solution will suffice. Second, they don’t test their DPL solutions thoroughly before implementing them, which can lead to unforeseen issues. Finally, they sometimes try to force a square peg into a round hole by using a DPL solution for a problem it wasn’t designed to solve. If you’re considering implementing a DPL solution, avoid these pitfalls and you’ll be more likely to succeed.

Data Loss Prevention (DLP) is a critical measure for companies that handle large amounts of sensitive data. By preventing the unauthorized sharing of this information, DLP can help protect your company from a data breach. There are a variety of ways to implement DLP, so it’s important to find the solution that best fits your needs. If you’re looking for a comprehensive solution to protect your data, consider implementing a DLP system, but be mindful of its limitations.

By Magda Chelly

Chief Security Officer | TEDx Speaker | Author & Keynote Speaker | IFSEC Global Top 20 Cybersecurity Influencer | Entrepreneur | PhD, S-CISO, CISSP, Cert SCI (General Insurance)

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Dr Magda CHELLY, CISSP, PhD

Dr Magda CHELLY, CISSP, PhD

Cyberfeminist | Entrepreneur | Former CISO | PhD, CISSP, S-CISO | CoFounder @R3sp_Cyb3r | @womenoncyber | Documentary The Dark Web on @myCanal