The Ultimate Manual to Data Loss Prevention Policies

Human error is the cause of data loss in 29% of cases. This speaks volumes about where organization owners should turn their attention: the employees.

Luckily, data loss prevention policies—ones that really defines how to share and protect data safely—can serve as guidelines to help businesses and their staff make sense of what they’re doing wrong and how they can improve data security.

This Nira guide will walk you through data loss prevention policies, and how you can use them to prevent data breaches and protect your sensitive information. Let’s take a look.

What Are Data Loss Prevention Policies Anyway?

A data loss prevention policy states how businesses can protect and share their data in a safe environment. It includes clear and easy-to-understand guidelines regarding how to use data in decision-making without accidentally exposing them to anyone who doesn’t have the authority to access it.

The whole point of data loss prevention (DLP) is to have a set of tools and processes that can prevent sensitive data from getting lost, misused, or accessed by unauthorized users. A DLP policy helps enforce this, making it easier for organizations to detect and prevent data breaches, exfiltration, or accidental destruction of sensitive data.

Why Are Data Loss Prevention Policies Important?

Most data security practices are focused on preventing malicious attacks on an organization‘s networks, with very few considering an employee’s role in data security. But as noted above, human error is a huge factor in data loss as well–close to a third of instances of lost information happen due to a mistake an internal employee made.

Moreover, today employees have more ways to access and share organizational data than they did before because of the distributed nature of modern computing. While this has certainly made our lives more convenient, it also makes accidental data loss a serious problem.

Then, of course, is the fact that you can store data in the cloud and other remote locations. With the pandemic forcing workforces to go remote, the number of employees working from remote locations continues to increase and so does the frequency of access to sensitive data from vulnerable laptops and mobile devices.

But there’s also good news.

Thanks to increased data security awareness, data collection and use are coming under increased regulatory scrutiny.

This has made companies more mindful, causing them to develop comprehensive data loss prevention policies to prevent critical data from being improperly accessed or deleted. A data loss prevention policy is important for the following three reasons:

  • Ensure compliance: Various government laws regulate how organizations can collect and secure personally identifiable information. With a data loss prevention policy, it becomes easier for organizations to comply with data regulation and report information in compliance audits.
  • Intellectual property: A data loss prevention policy also defines proprietary information and trade secrets that must be protected from unauthorized access. This clarity makes it easier for founders and staff to differentiate between important and unimportant data.
  • Data visibility: A data loss prevention policy monitors how stakeholders access and interact with data, giving organizations valuable insights to further strengthen their data security strategies.

They say knowledge is power. In the world of data security, this couldn’t be truer. Data loss prevention policies provide you with the knowledge that can help you share and store your data safely and help comply with the various government regulations.

How Data Loss Prevention Policies Work

To create an effective data loss prevention policy, organizations must understand what data is being collected and stored. Otherwise, the whole purpose of developing one would be defeated.

The best data loss prevention policies are drafted based on a business’s unique security environment, which is then communicated to employees. Once you have a DLP policy on paper, you can shift your attention to configuring appropriate policies in your DLP system.

Generally, a DLP system has a set of specific rules that should be strictly followed by the organization. Each rule consists of a condition and the action that the said organization must take when that condition is met. Also, rules have to be ranked by priority.

Here’s an at-a-glance view of the three elements in a typical DLP policy:

  • Location — Where will the DLP policy be enforced?
  • Condition — What parameters does the DLP policy want to search to prevent data loss?
  • Action — If a situation meets the above conditions, what action should an organization take to prevent data loss?

As you can see, the whole thing is interconnected. DLP policies are essentially a plan of action that organizations can enforce to control data loss.

Let’s explain this with the help of an example.

Suppose your organization develops a DLP policy to detect information that must be protected according to GDPR. Here’s how your data policy will look in this situation:

  • Location — This will be wherever the personal information is stored
  • Conditions — There can be three possible parameters here:

The user isn’t using the data as agreed
There are old and irrelevant data that should be deleted to ensure compliance
Personal data is being stored in a different and secure location

  • Action — As the actions should correspond to the conditions, you can delete any data that violates GDPR. Personal data can also be blocked if stored in an unverified environment.

Determining the Right Detection Techniques

Another crucial aspect of data loss prevention policies is detecting confidential information in a data stream. For this purpose, your policy should outline the different methods used by different systems that may apply to the situation. Some of these are:

  • Using the text analysis method
  • Creating digital fingerprints of protected and sensitive information
  • Affixing tags to protected and sensitive information
  • Identifying certain keywords and regular expressions commonly found in different types of sensitive documents, such as financial statements, contracts, and so on

Accuracy is incredibly critical here. False negatives (failure to spot information that is actually sensitive) can cause undetected leaks. Similarly, false positives (reporting data that isn’t actually sensitive) will waste your security team’s resources and can cause conflicts with users who are falsely accused of improper behavior.

You need a solid DLP policy, complete with a reliable DLP solution, to minimize false negatives and false positives.

How to Get Started With Data Loss Prevention Policies

Data loss prevention techniques have to be used continuously to enforce data usage policies. Here’s a step-by-step rundown of how to get started with a DLP policy and protect your sensitive data from internal and external threats.

Step 1: Identify and Classify Sensitive Data

You must know what type of data you have in order to protect it effectively.

We have data discovery technology available that can scan your data repositories and report on the findings. This will give you visibility into data you are required to protect as per the government.

Additionally, using data discovery and data classification technology enables you to control user data access and avoid storing sensitive data in a secure location. In turn, this reduces data leak and data loss risks. make sure you clearly label all critical and sensitive data with a digital signature, denoting its classification. This way, you can protect it according to its value to your organization.

You can keep updating classification as data is created, modified, stored, or transmitted. While you’re at it, make sure there are necessary controls in place to prevent users from falsifying classification levels. We highly recommend only allowing privileged users to be able to downgrade the classification of data.

Step 2: Create and Implement Access Control Lists

Access control lists (ACLs) indicate who can access what resource and at what level, and are generally an internal part of an operating system or application.

ACLs can be based on whitelists or blacklists. While the former refers to a list of items that are allowed (Eg: a list of websites users are allowed to visit during work hours), the latter is a list of prohibited things (Eg: software that users are forbidden from installing on client computers).

Implement access controls in every application that has role-based access control, such as active directory groups and delegation. This will help you create an even stronger data classification policy.

Step 3: Understand When Your Data Is at Risk

When data is distributed to user devices or shared with partners, customers, and the supply chain, different risks are created. In such cases, your data is often at the highest risk at the moment of use on endpoints. This can include attaching data to an email or moving it to a removable storage device.

You must have a robust data loss prevention program that can account for the mobility of data and the moments when data is put at risk.

Step 4: Carefully Monitor All Data Movement

The best way to understand which factors put your data at risk is through understanding how data is used and identifying existing behavior. Without this knowledge, you cannot develop appropriate policies that mitigate data loss risks while simultaneously allowing appropriate data use.

Keep in mind that not all data movement represents data loss. But many actions can certainly increase risks related to data loss. It’s why you should monitor all data movement to gain more visibility into what is happening to your sensitive data, which will then help you determine the scope of the issues that your DLP policies must address.

Step 5: Communicate and Develop Controls

Next, you must work with business line managers to understand how your data is being put at risk and accordingly create controls to control it.

Data usage controls can be straightforward at the beginning of a DLP initiative, where it targets the most common risky behaviors. However, as the data loss prevention program matures, you must develop more granular, fine-tuned controls to eliminate risks.

Step 6: Train and Guide Employees

Once you have a better understanding of the circumstances under which data is moved, you can train your employees to improve your chances of mitigating risks caused due to accidental data loss.

Employees often don’t recognize their actions can cause data loss, but the good news is they are willing to self-correct when explaining where they are going wrong. Keeping this in mind, you should hold regular training sessions to help them understand how to handle sensitive data and identify potential risks to systems. Even better if you can hire experts who your employees can turn to for guidance


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Bryan Wise
Bryan Wise,
Former VP of IT at GitLab

Incredible companies use Nira