How to Approach Data Loss Prevention (DLP)? Identifying the Best Practices

Sudeep Srivastava December 27, 2022
Data loss prevention

With entrepreneurs from across different sectors waking up everyday with the news of their competitors getting hacked, they are left wondering, “Am I next?”.

This fear that is festering among business owners is not completely irrational. According to an IBM report, the cost of data breach has increased 2.6% from $4.24 million in 2021 to $4.35 million in 2022. Unfortunately, cyber attacks are getting more complex every passing day, putting not just money but also enterprises’ reputation at stake.

As a respite, there are many solutions on the market to help enterprises prevent themselves from becoming a data breach victim. In this guide we are going to look into a pivotal approach of enterprise data safeguarding – data loss prevention (DLP).

It has everything you need to know around what is data loss prevention, the way it works, and the implementation of a DLP strategy in your business.

What is data loss prevention (DLP)?

Data loss prevention consists of approaches – tools and strategies – aimed at preventing enterprise data from getting lost or misused. It revolves around protecting data in four states –

Data loss prevention

  • Data at rest – It refers to the location where data is located – the database or network and if it is encrypted.
  • Data in use – It is the set of data which is being accessed at any point in time.
  • Data in motion – It is the data which is in transit – moving between databases and networks.
  • Cloud DLP – It is the set of data that resides on the cloud or email.

Efficient data protection calls for knowing the crucial data you store and then having rules and policies around storing, utilizing, and moving that data. To keep in line with the growing complexity of handling cybersecurity, the DLP solutions have gone through a remarkable change in some years. We have seen advancements happening across data discovery, enforcement, exfiltration notification, and data management, etc.

Now that we have done a quick relook at what DLP solutions are, let us get down to the elements that make it work. Only when you have a high-level idea of how the approach works, will you be able to introduce it in your business processes.

Adopt a DLP solution for your business

How does data loss prevention work?

The data loss prevention techniques utilize multiple strategies based on types of configuration and tools. However, at the base of these strategies lies an effective data loss prevention process. Here are some ways the DLP works

  1. Rule-driven matching – The data loss prevention systems use established patterns for finding data that match the specific rules.
  2. Database fingerprinting –  The DLP plan searches for an exact match of the structured data which has been given by the client.
  3. File matching – The DLP software looks for data based on hashes instead of content.
  4. Partial document matching – The DLP software searches for files that partially match the pre-set patterns.
  5. Data analysis – The DLP solutions utilizes advanced tools like AI and Machine Learning for identifying sensitive information, resulting in better data accuracy and identification of context around the findings.

The idea behind data loss prevention solutions is simple – to know how data is being used, how it is moving and following compliances like HIPAA and GDPR, and the data loss prevention software should give notification when a suspicious activity is detected so that it can be investigated. However, for the software to work as intended, it is critical to have well-defined data loss prevention procedures and practices in place.

Best practices to approach data loss prevention solutions

Data loss prevention best practices can vary for different organizations but the end goal is always to protect sensitive data from getting into the wrong hands. Here are some practices that we vouch upon when it comes to preventing data loss for businesses.

Data loss prevention solutions

  • Classify data

The first step of protecting data lies in knowing the types of data you have and which of them classifies as sensitive information. Data loss prevention systems should make it easy for businesses to classify and label private data with an encrypted digital signature. Once the data are properly classified, admins are able to locate and assess them on a need basis.

A crucial part of this activity is building an access control list where who can access what data is clearly specified. By adding an encryption on the sensitive information, businesses can get notified when someone without access tries to access the data.

  • Use data encryption

Another one of the data loss prevention best practices lies in encrypting all the crucial data when it is in rest or in transit. You should build a process where users with limited access can only get an unencrypted copy of the data which contains partial information, while on the other hand, users with complete access to the data can view or modify it in a way that the system tracks all the changes along with the users details.

While this is about software-based encryption, if your data is stored locally, you will need to focus on hardware-based encryption as well which includes storing certificates and cryptographic keys.

  • Build a cloud data loss prevention policy

With over 60% of global corporate data being stored on the cloud, it has become critical to secure data in the cloud and form cloud DLP policy best practices.

Share of corporate data stored in the cloud in organization worldwide from 2015 to 2022

While a majority of the cloud platforms like AWS and Google have in-built protocols around security to keep information safe, businesses tend to assume that they won’t need any cloud specific encryption method. However, the benefits of accessing data from anywhere opens the door for hackers to get creative.

The solution to this lies in using data loss prevention tools and APIs that lowers data risk with de-identification, obfuscation methods and inspect, classifying data into sensitive information seamlessly through the power of machine learning.

  • Keep systems updated

One of the most obvious, yet overlooked data loss prevention strategies is keeping all the systems updated. Businesses, specially startups tend to pass on frequent updating of software and hardware owing to the time and sometimes money it takes to update and upgrade them, opening themselves to hacks and data breach.

Another aspect to consider in terms of keeping systems updated is that while it is okay to automate updates for antivirus software, the updates that require infrastructure changes need to be studied thoroughly. This would help ensure that the functionalities are not compromised and zero vulnerabilities get introduced in the system.

  • Educate stakeholders

Your data loss prevention best practices will be as strong as your least security-educated stakeholder. Invest in educating your stakeholders and data users on how to manage data in order to ensure its security and the implications of not taking care of the sensitive information.

Only when you teach the importance of data loss prevention strategy will the users be able to take it up on priority.

The success of these key steps to improve DLP we just looked into is highly dependent on consistency you are able to maintain in adapting the data loss prevention practices. But knowing when to start can be even more challenging.

In any ongoing process, how do you decide when to implement and bring data loss prevention methods into focus? Let us look into that through these scenarios.

Event A

A medical company processes patients’ data for a hospital. They know that HIPAA data is there in the file server, but are not sure of its exact location.

Solution: Implement DLP at Rest

The strategy would consist of using data loss prevention tools that offer a discovery scan of the unstructured data that would crawl the file server and find data matching the HIPAA keywords. Once done, a notification will be sent to the DLP database.

Event B

The Operations Manager finds that her team members email themselves sensitive information to work over the weekends.

Solution: Implement DLP For Network

Here multiple data loss prevention techniques can be employed to save files from getting uploaded to Gmail, one of which would be DLP for Network. Another approach, DLP for Endpoint, can help identify http/https with advanced application configuration.

Event C

The Marketing team is having issues because of the inability to store their presentations on USB

Solution: Implement the DLP for Endpoints

The best idea would be to give an exception to the marketing team by using DLP for Endpoints to whitelist them in the Active Directory plan.

Scenario D

The CEO wants to know when the RFP moved from the original location.

Solution: Build a DLP Policy

Create a process that enables detection of the exact match of the document and notifies when it is moved.

Implement the best data loss prevention practices

With this, we have looked into the best practices and the timings of data loss prevention strategy implementation. What remains now is to create a DLP adoption program on a step-by-step level.

Different organizations follow different approaches for building a DLP program. At Appinventiv, we follow a thorough process that doesn’t just answer how to implement data loss prevention but also helps you create a standard for users to follow and prevent data breach incidents.

What are some ways to prevent data loss? Appinventiv approach

At Appinventiv we take data loss prevention (DLP) very seriously. In addition to following a security-first development approach, we help enterprises navigate data loss prevention fundamentals. Here’s the process we follow as part of our cloud data services.

Step 1: Scope out the program

First, we start with understanding the business needs through identification and prioritization of data risks, following which we gather the data which has to be protected and verify the data owners.

Next, our team makes a data flow map to see where the data is originating from, location where it’s getting stored and how it is moving between networks.

Step 2: Build governance activities

We then identify and better the business practices for data handling. Example, we build a consolidated list of the accepted programs, protocols, and data-management process by working with the legal team. One thing we highlight to our clients here is that the DLP requires constant upgrades as businesses require change.

Step 3: Design the initial architecture

Next, we find DLP tools that would offer the necessary data control. Oftentime, it is not possible to cover every aspect through one vendor offering, which means you will have to incorporate multiple DLP technologies in your business processes – a process that becomes easy with the data flow mapping activity we do in the first step.

Step 4: Start addressing dependencies

The capability of DLP tools to find data loss instances can get confused by multiple users’ dependencies, both process-based and technical. We build your DLP’s effectiveness to address those individual dependencies according to different levels of access. For example, we only give access to business data when an authorized user asks for it for a real business need.

Step 5: Deploy and evolve

We use a “monitoring only” implementation of the DLP so that we are able to test if the process is working and then refine them according to business needs. At this stage, we make it a point to communicate with the users and keep them informed about what is happening with their data and ways they can continue to make it secure.

FAQs about data loss prevention (DLP)

Q. What is a data loss prevention policy?

A. A data loss prevention (DLP) policy consists of strategies to prevent enterprises from data breach instances. It combines tools and processes which safeguard businesses from data misuse, leakage, and loss.

Q. Why is data loss prevention important?

A. There are a number of reasons why having a data loss prevention policy is important – you don’t know where the data is stored, you don’t have a plan in place for data prevention from intruders, you are concerned about fines and reputation, and you want to maintain compliances’ requirements.

Q. What are the types of data loss prevention?

A. Here are the different types of DLPs –

  • Network DLP: It monitors and safeguard data in use, at rest, and in motion within the company’s network.
  • Endpoint DLP: It looks into all the endpoints that consist of computers, servers, and mobile devices on which the data gets used, is saved, or is moved.
  • Cloud DLP: It is a part of the Network DLP, which is designed for protecting the businesses that use cloud for storing data.

Parting Notes

An ever-evolving data threat landscape combined with tightening regulations has elevated the need for better data management. As a result, businesses have started looking for answers to how data loss prevention can be improved?

The best practices that we covered in the article can help bring you on the right path when it comes to protecting data, however the key to success will lie in consistency and a regular investment in scaling up the offering. This will ensure that you are on the right path when it comes to establishing that your data loss prevention software is in line with the future’s use cases for DLP.

After everything’s said and done, we know how difficult it can be to find answers to micro-level questions like which types of data loss prevention which fit in which situation, which are the best data loss prevention tools, or how much each round of data loss prevention planning costs.  We can help you find answers to these questions. Get in touch with our security experts today.

THE AUTHOR
Sudeep Srivastava
Co-Founder and Director
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