Identifying the Best Practices for Data Loss Prevention
This fear, which has taken root among business owners, is not entirely irrational. According to an IBM report, the cost of data breaches rose 2.6 percent from $4.24 million in 2021 to $4.35 million in 2022. Unfortunately, cyberattacks are getting more complex every day, putting not only money but also the reputations of businesses at stake.
As a reprieve, many solutions are on the market to help businesses not fall victim to a data breach. In this guide, we look at a key approach to protecting enterprise data – data loss prevention (DLP).
Here is everything you need to know about data loss prevention, how it works, and how to implement a DLP strategy in your business.
What is data loss prevention (DLP)?
Data loss prevention consists of approaches – tools and strategies – to prevent the loss or misuse of corporate data. It involves protecting data in four states.
Data at rest is where it resides – a database or network, and if it is encrypted.
Data in use is the set of data that is accessed at any given time.
Data in motion is data that is in transit – moving between databases and networks.
Cloud DLP is a set of data that resides in the cloud or in email.
Adequate data protection requires knowledge of the critical data you store, as well as rules and policies for storing, using, and moving that data. To keep up with the growing complexity of cybersecurity, DLP solutions have undergone significant changes over the years. We’ve seen advances in data discovery, enforcement, exfiltration notification, data management, etc.
Now that we’ve taken a brief look at what DLP solutions are let’s move on to the elements that make them work. Only when you understand how this approach works will you be able to implement it in your business processes.
How does data lose prevention work?
Data loss prevention methods use many strategies based on configuration types and tools. However, at the heart of these strategies is an effective data loss prevention process. Here are some ways DLP works
Rule-driven matching – The data loss prevention systems use established patterns for searching for data that matches specific rules.
Database fingerprinting – A DLP plan looks for exact matches to structured data that the client has provided.
File matching – DLP software searches for data based on hashes, not content.
Partial document matching – DLP software searches for files that partially match specified patterns.
Data analysis – DLP solutions use advanced tools, such as artificial intelligence and machine learning, to identify sensitive information, which improves data accuracy and provides context around the data found.
The idea behind data loss prevention solutions is simple – know how data is being used, how it is being moved, and comply with requirements such as HIPAA and GDPR. Data lose prevention software should issue alerts when suspicious activity is detected so that it can be investigated. However, for software to work as intended, it is critical to have well-defined data loss prevention procedures and practices.
Best practices approach to data loss prevention solutions
Data loss prevention best practices may vary from organization to organization, but the ultimate goal is always to protect sensitive data from falling into the wrong hands. Here are some practices we vouch for when it comes to data loss prevention for businesses.
Classify your data
The first step in data protection is to know what types of data you have and which ones are classified as sensitive information. Data loss prevention systems should make it easy for businesses to classify and label sensitive data with an encrypted digital signature. Once the data is appropriately classified, administrators can find and evaluate it as needed.
A critical part of this effort is creating an access control list that indicates who can access what data. By adding encryption to sensitive information, businesses can be notified when someone without access attempts to access the data.
Use data encryption
Another of the best methods to prevent data loss is to encrypt all important data when it is at rest or in transit. You should structure the process so that limited-access users can only get an unencrypted copy of the data containing partial information, while full-access users can view or modify the data so that the system tracks all changes along with the users’ data.
While this is about software encryption, if your data is stored locally, you need to focus on hardware encryption, which includes storing certificates and cryptographic keys.
Develop a policy to prevent data loss in the cloud
With more than 60% of global enterprise data stored in the cloud, it has become critical to secure data in the cloud and establish cloud DLP policy best practices.
While most cloud platforms, such as AWS and Google, have built-in security protocols to keep information secure, companies tend to believe they don’t need any cloud-specific encryption method. However, the benefits of accessing data from anywhere open the door for hackers to get creative.
The solution to this problem lies in data loss prevention tools and APIs that reduce the risk of data loss through de-identification, obfuscation, and validation methods, classifying data into sensitive information without extra effort through machine learning.
Updating all systems is one of the most obvious but overlooked strategies for preventing data loss. Enterprises, especially startups, typically refuse to update software and hardware frequently because of the time and sometimes money it takes to update and upgrade them, opening themselves up to hacks and data breaches.
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.
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 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 critical steps to improve the DLP we just looked into is highly dependent on the consistency you can maintain in adapting the data loss prevention practices. But knowing when to start can be even more challenging.
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 we covered in the article can help bring you on the right path to protecting data. However, consistency and regular investment in scaling up the offering will be the key to success. This will ensure that you are on the right path to establishing that your data loss prevention software is in line with the future 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.